Overcoming Redis Limitations: The Dragonfly DB Approach

Data Engineering Podcast

#Tech #Tobias Macey #Data Engineering #Big Data #Database #Data Pipeline #Podcasting Education

1:00:36

Do you have fond childhood memories of summer camp? For a chance at $250,000, campers must compete in a series of summer camp-themed challenges to prove that they are unbeatable, unhateable, and unbreakable. Host Chris Burns is joined by the multi-talented comedian Dana Moon to recap the first five episodes of season one of Battle Camp . Plus, Quori-Tyler (aka QT) joins the podcast to dish on the camp gossip, team dynamics, and the Watson to her Sherlock Holmes. Leave us a voice message at www.speakpipe.com/WeHaveTheReceipts Text us at (929) 487-3621 DM Chris @FatCarrieBradshaw on Instagram Follow We Have The Receipts wherever you listen, so you never miss an episode. Listen to more from Netflix Podcasts.…

about a year ago 43:58

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Hello and welcome to the Data Engineering Podcast, the show about modern data management
Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.
Your host is Tobias Macey and today I'm interviewing Roman Gershman about building a high-speed in-memory database and the impact of the performance gains on data applications

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what DragonflyDB is and the story behind it?
What is the core problem/use case that is solved by making a "faster Redis"?
The other major player in the high performance key/value database space is Aerospike. What are the heuristics that an engineer should use to determine whether to use that vs. Dragonfly/Redis?
Common use cases for Redis involve application caches and queueing (e.g. Celery/RQ). What are some of the other applications that you have seen Redis/Dragonfly used for, particularly in data engineering use cases?
There is a piece of tribal wisdom that it takes 10 years for a database to iron out all of the kinks. At the same time, there have been substantial investments in commoditizing the underlying components of database engines. Can you describe how you approached the implementation of DragonflyDB to arive at a functional and reliable implementation?
What are the architectural elements that contribute to the performance and scalability benefits of Dragonfly?
- How have the design and goals of the system changed since you first started working on it?
For teams who migrate from Redis to Dragonfly, beyond the cost savings what are some of the ways that it changes the ways that they think about their overall system design?
What are the most interesting, innovative, or unexpected ways that you have seen Dragonfly used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on DragonflyDB?
When is DragonflyDB the wrong choice?
What do you have planned for the future of DragonflyDB?

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story.

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

465 episodes

Overcoming Redis Limitations: The Dragonfly DB Approach

Data Engineering Podcast

71 subscribers

published about a year ago

MP3•Episode home

Fetch error

Hmmm there seems to be a problem fetching this series right now. Last successful fetch was on May 13, 2025 23:55 (5d ago)

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.
Your host is Tobias Macey and today I'm interviewing Roman Gershman about building a high-speed in-memory database and the impact of the performance gains on data applications

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what DragonflyDB is and the story behind it?
What is the core problem/use case that is solved by making a "faster Redis"?
The other major player in the high performance key/value database space is Aerospike. What are the heuristics that an engineer should use to determine whether to use that vs. Dragonfly/Redis?
Common use cases for Redis involve application caches and queueing (e.g. Celery/RQ). What are some of the other applications that you have seen Redis/Dragonfly used for, particularly in data engineering use cases?
There is a piece of tribal wisdom that it takes 10 years for a database to iron out all of the kinks. At the same time, there have been substantial investments in commoditizing the underlying components of database engines. Can you describe how you approached the implementation of DragonflyDB to arive at a functional and reliable implementation?
What are the architectural elements that contribute to the performance and scalability benefits of Dragonfly?
- How have the design and goals of the system changed since you first started working on it?
For teams who migrate from Redis to Dragonfly, beyond the cost savings what are some of the ways that it changes the ways that they think about their overall system design?
What are the most interesting, innovative, or unexpected ways that you have seen Dragonfly used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on DragonflyDB?
When is DragonflyDB the wrong choice?
What do you have planned for the future of DragonflyDB?

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story.

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

465 episodes

#Tech #Tobias Macey #Data Engineering #Big Data #Database #Data Pipeline #Podcasting Education

All episodes

Data Engineering Podcast

1
Balancing Off-the-Shelf and Custom Solutions in Data Engineering 46:05

5 days ago46:05

46:05

Summary In this episode of the Data Engineering Podcast Tulika Bhatt, a senior software engineer at Netflix, talks about her experiences with large-scale data processing and the future of data engineering technologies. Tulika shares her journey into the data engineering field, discussing her work at BlackRock and Verizon before joining Netflix, and explains the challenges and innovations involved in managing Netflix's impression data for personalization and user experience. She highlights the importance of balancing off-the-shelf solutions with custom-built systems using technologies like Spark, Flink, and Iceberg, and delves into the complexities of ensuring data quality and observability in high-speed environments, including robust alerting strategies and semantic data auditing. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Tulika Bhatt about her experiences working on large scale data processing and her insights on the future trajectory of the supporting technologies Interview Introduction How did you get involved in the area of data management? Can you start by outlining the ways that operating at large scale change the ways that you need to think about the design of data systems? When dealing with small-scale data systems it can be feasible to have manual processes. What are the elements of large scal data systems that demand autopmation? How can those large-scale automation principles be down-scaled to the systems that the rest of the world are operating? A perennial problem in data engineering is that of data quality. The past 4 years has seen a significant growth in the number of tools and practices available for automating the validation and verification of data. In your experience working with high volume data flows, what are the elements of data validation that are still unsolved? Generative AI has taken the world by storm over the past couple years. How has that changed the ways that you approach your daily work? What do you see as the future realities of working with data across various axes of large scale, real-time, etc.? What are the most interesting, innovative, or unexpected ways that you have seen solutions to large-scale data management designed? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data management across axes of scale? What are the ways that you are thinking about the future trajectory of your work?? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links BlackRock Spark Flink Kafka Cassandra RocksDB Netflix Maestro workflow orchestrator Pagerduty Iceberg The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

Data Engineering Podcast

1
StarRocks: Bridging Lakehouse and OLAP for High-Performance Analytics 59:41

14 days ago59:41

59:41

Summary In this episode of the Data Engineering Podcast Sida Shen, product manager at CelerData, talks about StarRocks, a high-performance analytical database. Sida discusses the inception of StarRocks, which was forked from Apache Doris in 2020 and evolved into a high-performance Lakehouse query engine. He explains the architectural design of StarRocks, highlighting its capabilities in handling high concurrency and low latency queries, and its integration with open table formats like Apache Iceberg, Delta Lake, and Apache Hudi. Sida also discusses how StarRocks differentiates itself from other query engines by supporting on-the-fly joins and eliminating the need for denormalization pipelines, and shares insights into its use cases, such as customer-facing analytics and real-time data processing, as well as future directions for the platform. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Sida Shen about StarRocks, a high performance analytical database supporting shared nothing and shared data patterns Interview Introduction How did you get involved in the area of data management? Can you describe what StarRocks is and the story behind it? There are numerous analytical databases on the market. What are the attributes of StarRocks that differentiate it from other options? Can you describe the architecture of StarRocks? What are the "-ilities" that are foundational to the design of the system? How have the design and focus of the project evolved since it was first created? What are the tradeoffs involved in separating the communication layer from the data layers? The tiered architecture enables the shared nothing and shared data behaviors, which allows for the implementation of lakehouse patterns. What are some of the patterns that are possible due to the single interface/dual pattern nature of StarRocks? The shared data implementation has cacheing built in to accelerate interaction with datasets. What are some of the limitations/edge cases that operators and consumers should be aware of? StarRocks supports management of lakehouse tables (Iceberg, Delta, Hudi, etc.), which overlaps with use cases for Trino/Presto/Dremio/etc. What are the cases where StarRocks acts as a replacement for those systems vs. a supplement to them? The other major category of engines that StarRocks overlaps with is OLAP databases (e.g. Clickhouse, Firebolt, etc.). Why might someone use StarRocks in addition to or in place of those techologies? We would be remiss if we ignored the dominating trend of AI and the systems that support it. What is the role of StarRocks in the context of an AI application? What are the most interesting, innovative, or unexpected ways that you have seen StarRocks used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on StarRocks? When is StarRocks the wrong choice? What do you have planned for the future of StarRocks? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links StarRocks CelerData Apache Doris SIMD == Single Instruction Multiple Data Apache Iceberg ClickHouse Podcast Episode Druid Firebolt Podcast Episode Snowflake BigQuery Trino Databricks Dremio Data Lakehouse Delta Lake Apache Hive C++ Cost-Based Optimizer Iceberg Summit Tencent Games Presentation Apache Paimon Lance Podcast Episode Delta Uniform Apache Arrow StarRocks Python UDF Debezium Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

Data Engineering Podcast

1
Exploring NATS: A Multi-Paradigm Connectivity Layer for Distributed Applications 1:12:50

21 days ago1:12:50

1:12:50

Summary In this episode of the Data Engineering Podcast Derek Collison, creator of NATS and CEO of Synadia, talks about the evolution and capabilities of NATS as a multi-paradigm connectivity layer for distributed applications. Derek discusses the challenges and solutions in building distributed systems, and highlights the unique features of NATS that differentiate it from other messaging systems. He delves into the architectural decisions behind NATS, including its ability to handle high-speed global microservices, support for edge computing, and integration with Jetstream for data persistence, and explores the role of NATS in modern data management and its use cases in industries like manufacturing and connected vehicles. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Derek Collison about NATS, a multi-paradigm connectivity layer for distributed applications. Interview Introduction How did you get involved in the area of data management? Can you describe what NATS is and the story behind it? How have your experiences in past roles (cloud foundry, TIBCO messaging systems) informed the core principles of NATS? What other sources of inspiration have you drawn on in the design and evolution of NATS? (e.g. Kafka, RabbitMQ, etc.) There are several patterns and abstractions that NATS can support, many of which overlap with other well-regarded technologies. When designing a system or service, what are the heuristics that should be used to determine whether NATS should act as a replacement or addition to those capabilities? (e.g. considerations of scale, speed, ecosystem compatibility, etc.) There is often a divide in the technologies and architecture used between operational/user-facing applications and data systems. How does the unification of multiple messaging patterns in NATS shift the ways that teams think about the relationship between these use cases? How does the shared communication layer of NATS with multiple protocol and pattern adaptaters reduce the need to replicate data and logic across application and data layers? Can you describe how the core NATS system is architected? How have the design and goals of NATS evolved since you first started working on it? In the time since you first began writing NATS (~2012) there have been several evolutionary stages in both application and data implementation patterns. How have those shifts influenced the direction of the NATS project and its ecosystem? For teams who have an existing architecture, what are some of the patterns for adoption of NATS that allow them to augment or migrate their capabilities? What are some of the ecosystem investments that you and your team have made to ease the adoption and integration of NATS? What are the most interesting, innovative, or unexpected ways that you have seen NATS used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on NATS? When is NATS the wrong choice? What do you have planned for the future of NATS? Contact Info GitHub LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links NATS NATS JetStream Synadia Cloud Foundry TIBCO Applied Physics Lab - Johns Hopkins University Cray Supercomputer RVCM Certified Messaging TIBCO ZMS IBM MQ JMS == Java Message Service RabbitMQ MongoDB NodeJS Redis AMQP == Advanced Message Queueing Protocol Pub/Sub Pattern Circuit Breaker Pattern Zero MQ Akamai Fastly CDN == Content Delivery Network At Most Once At Least Once Exactly Once AWS Kinesis Memcached SQS Segment Rudderstack Podcast Episode DLQ == Dead Letter Queue MQTT == Message Queueing Telemetry Transport NATS Kafka Bridge 10BaseT Network Web Assembly RedPanda Podcast Episode Pulsar Functions mTLS AuthZ (Authorization) AuthN (Authentication) NATS Auth Callouts OPA == Open Policy Agent RAG == Retrieval Augmented Generation AI Engineering Podcast Episode Home Assistant Podcast.__init__ Episode Tailscale Ollama CDC == Change Data Capture gRPC The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

Data Engineering Podcast

1
Advanced Lakehouse Management With The LakeKeeper Iceberg REST Catalog 57:13

28 days ago57:13

57:13

Summary In this episode of the Data Engineering Podcast Viktor Kessler, co-founder of Vakmo, talks about the architectural patterns in the lake house enabled by a fast and feature-rich Iceberg catalog. Viktor shares his journey from data warehouses to developing the open-source project, Lakekeeper, an Apache Iceberg REST catalog written in Rust that facilitates building lake houses with essential components like storage, compute, and catalog management. He discusses the importance of metadata in making data actionable, the evolution of data catalogs, and the challenges and innovations in the space, including integration with OpenFGA for fine-grained access control and managing data across formats and compute engines. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Viktor Kessler about architectural patterns in the lakehouse that are unlocked by a fast and feature-rich Iceberg catalog Interview Introduction How did you get involved in the area of data management? Can you describe what LakeKeeper is and the story behind it? What is the core of the problem that you are addressing? There has been a lot of activity in the catalog space recently. What are the driving forces that have highlighted the need for a better metadata catalog in the data lake/distributed data ecosystem? How would you characterize the feature sets/problem spaces that different entrants are focused on addressing? Iceberg as a table format has gained a lot of attention and adoption across the data ecosystem. The REST catalog format has opened the door for numerous implementations. What are the opportunities for innovation and improving user experience in that space? What is the role of the catalog in managing security and governance? (AuthZ, auditing, etc.) What are the channels for propagating identity and permissions to compute engines? (how do you avoid head-scratching about permission denied situations) Can you describe how LakeKeeper is implemented? How have the design and goals of the project changed since you first started working on it? For someone who has an existing set of Iceberg tables and catalog, what does the migration process look like? What new workflows or capabilities does LakeKeeper enable for data teams using Iceberg tables across one or more compute frameworks? What are the most interesting, innovative, or unexpected ways that you have seen LakeKeeper used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on LakeKeeper? When is LakeKeeper the wrong choice? What do you have planned for the future of LakeKeeper? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links LakeKeeper SAP Microsoft Access Microsoft Excel Apache Iceberg Podcast Episode Iceberg REST Catalog PyIceberg Spark Trino Dremio Hive Metastore Hadoop NATS Polars DuckDB Podcast Episode DataFusion Atlan Podcast Episode Open Metadata Podcast Episode Apache Atlas OpenFGA Hudi Podcast Episode Delta Lake Podcast Episode Lance Table Format Podcast Episode Unity Catalog Polaris Catalog Apache Gravitino Podcast Episode Keycloak Open Policy Agent (OPA) Apache Ranger Apache NiFi The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

Data Engineering Podcast

1
Simplifying Data Pipelines with Durable Execution 39:49

5 weeks ago39:49

39:49

Summary In this episode of the Data Engineering Podcast Jeremy Edberg, CEO of DBOS, about durable execution and its impact on designing and implementing business logic for data systems. Jeremy explains how DBOS's serverless platform and orchestrator provide local resilience and reduce operational overhead, ensuring exactly-once execution in distributed systems through the use of the Transact library. He discusses the importance of version management in long-running workflows and how DBOS simplifies system design by reducing infrastructure needs like queues and CI pipelines, making it beneficial for data pipelines, AI workloads, and agentic AI. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Jeremy Edberg about durable execution and how it influences the design and implementation of business logic Interview Introduction How did you get involved in the area of data management? Can you describe what DBOS is and the story behind it? What is durable execution? What are some of the notable ways that inclusion of durable execution in an application architecture changes the ways that the rest of the application is implemented? (e.g. error handling, logic flow, etc.) Many data pipelines involve complex, multi-step workflows. How does DBOS simplify the creation and management of resilient data pipelines? How does durable execution impact the operational complexity of data management systems? One of the complexities in durable execution is managing code/data changes to workflows while existing executions are still processing. What are some of the useful patterns for addressing that challenge and how does DBOS help? Can you describe how DBOS is architected? How have the design and goals of the system changed since you first started working on it? What are the characteristics of Postgres that make it suitable for the persistence mechanism of DBOS? What are the guiding principles that you rely on to determine the boundaries between the open source and commercial elements of DBOS? What are the most interesting, innovative, or unexpected ways that you have seen DBOS used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on DBOS? When is DBOS the wrong choice? What do you have planned for the future of DBOS? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links DBOS Exactly Once Semantics Temporal Sempahore Postgres DBOS Transact Python Typescript Idempotency Keys Agentic AI State Machine YugabyteDB Podcast Episode CockroachDB Supabase Neon Podcast Episode Airflow The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

Data Engineering Podcast

1
Overcoming Redis Limitations: The Dragonfly DB Approach 43:58

7 weeks ago43:58

43:58

Summary In this episode of the Data Engineering Podcast Roman Gershman, CTO and founder of Dragonfly DB, explores the development and impact of high-speed in-memory databases. Roman shares his experience creating a more efficient alternative to Redis, focusing on performance gains, scalability, and cost efficiency, while addressing limitations such as high throughput and low latency scenarios. He explains how Dragonfly DB solves operational complexities for users and delves into its technical aspects, including maintaining compatibility with Redis while innovating on memory efficiency. Roman discusses the importance of cost efficiency and operational simplicity in driving adoption and shares insights on the broader ecosystem of in-memory data stores, future directions like SSD tiering and vector search capabilities, and the lessons learned from building a new database engine. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Roman Gershman about building a high-speed in-memory database and the impact of the performance gains on data applications Interview Introduction How did you get involved in the area of data management? Can you describe what DragonflyDB is and the story behind it? What is the core problem/use case that is solved by making a "faster Redis"? The other major player in the high performance key/value database space is Aerospike. What are the heuristics that an engineer should use to determine whether to use that vs. Dragonfly/Redis? Common use cases for Redis involve application caches and queueing (e.g. Celery/RQ). What are some of the other applications that you have seen Redis/Dragonfly used for, particularly in data engineering use cases? There is a piece of tribal wisdom that it takes 10 years for a database to iron out all of the kinks. At the same time, there have been substantial investments in commoditizing the underlying components of database engines. Can you describe how you approached the implementation of DragonflyDB to arive at a functional and reliable implementation? What are the architectural elements that contribute to the performance and scalability benefits of Dragonfly? How have the design and goals of the system changed since you first started working on it? For teams who migrate from Redis to Dragonfly, beyond the cost savings what are some of the ways that it changes the ways that they think about their overall system design? What are the most interesting, innovative, or unexpected ways that you have seen Dragonfly used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on DragonflyDB? When is DragonflyDB the wrong choice? What do you have planned for the future of DragonflyDB? Contact Info GitHub LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links DragonflyDB Redis Elasticache ValKey Aerospike Laravel Sidekiq Celery Seastar Framework Shared-Nothing Architecture io_uring midi-redis Dunning-Kruger Effect Rust The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

Data Engineering Podcast

1
Bringing AI Into The Inner Loop of Data Engineering With Ascend 52:47

8 weeks ago52:47

52:47

Summary In this episode of the Data Engineering Podcast Sean Knapp, CEO of Ascend.io, explores the intersection of AI and data engineering. He discusses the evolution of data engineering and the role of AI in automating processes, alleviating burdens on data engineers, and enabling them to focus on complex tasks and innovation. The conversation covers the challenges and opportunities presented by AI, including the need for intelligent tooling and its potential to streamline data engineering processes. Sean and Tobias also delve into the impact of generative AI on data engineering, highlighting its ability to accelerate development, improve governance, and enhance productivity, while also noting the current limitations and future potential of AI in the field. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Sean Knapp about how Ascend is incorporating AI into their platform to help you keep up with the rapid rate of change Interview Introduction How did you get involved in the area of data management? Can you describe what Ascend is and the story behind it? The last time we spoke was August of 2022 . What are the most notable or interesting evolutions in your platform since then? In that same time "AI" has taken up all of the oxygen in the data ecosystem. How has that impacted the ways that you and your customers think about their priorities? The introduction of AI as an API has caused many organizations to try and leap-frog their data maturity journey and jump straight to building with advanced capabilities. How is that impacting the pressures and priorities felt by data teams? At the same time that AI-focused product goals are straining data teams capacities, AI also has the potential to act as an accelerator to their work. What are the roadblocks/speedbumps that are in the way of that capability? Many data teams are incorporating AI tools into parts of their workflow, but it can be clunky and cumbersome. How are you thinking about the fundamental changes in how your platform works with AI at its center? Can you describe the technical architecture that you have evolved toward that allows for AI to drive the experience rather than being a bolt-on? What are the concrete impacts that these new capabilities have on teams who are using Ascend? What are the most interesting, innovative, or unexpected ways that you have seen Ascend + AI used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on incorporating AI into the core of Ascend? When is Ascend the wrong choice? What do you have planned for the future of AI in Ascend? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links Ascend Cursor AI Code Editor Devin GitHub Copilot OpenAI DeepResearch S3 Tables AWS Glue AWS Bedrock Snowpark Co-Intelligence : Living and Working with AI by Ethan Mollick (affiliate link) OpenAI o3 The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

Data Engineering Podcast

1
Astronomer's Role in the Airflow Ecosystem: A Deep Dive with Pete DeJoy 51:41

9 weeks ago51:41

51:41

Summary In this episode of the Data Engineering Podcast Pete DeJoy, co-founder and product lead at Astronomer, talks about building and managing Airflow pipelines on Astronomer and the upcoming improvements in Airflow 3. Pete shares his journey into data engineering, discusses Astronomer's contributions to the Airflow project, and highlights the critical role of Airflow in powering operational data products. He covers the evolution of Airflow, its position in the data ecosystem, and the challenges faced by data engineers, including infrastructure management and observability. The conversation also touches on the upcoming Airflow 3 release, which introduces data awareness, architectural improvements, and multi-language support, and Astronomer's observability suite, Astro Observe, which provides insights and proactive recommendations for Airflow users. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Pete DeJoy about building and managing Airflow pipelines on Astronomer and the upcoming improvements in Airflow 3 Interview Introduction Can you describe what Astronomer is and the story behind it? How would you characterize the relationship between Airflow and Astronomer? Astronomer just released your State of Airflow 2025 Report yesterday and it is the largest data engineering survey ever with over 5,000 respondents. Can you talk a bit about top level findings in the report? What about the overall growth of the Airflow project over time? How have the focus and features of Astronomer changed since it was last featured on the show in 2017? Astro Observe GA’d in early February, what does the addition of pipeline observability mean for your customers? What are other capabilities similar in scope to observability that Astronomer is looking at adding to the platform? Why is Airflow so critical in providing an elevated Observability–or cataloging, or something simlar - experience in a DataOps platform? What are the notable evolutions in the Airflow project and ecosystem in that time? What are the core improvements that are planned for Airflow 3.0? What are the most interesting, innovative, or unexpected ways that you have seen Astro used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Airflow and Astro? What do you have planned for the future of Astro/Astronomer/Airflow? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links Astronomer Airflow Maxime Beauchemin MongoDB Databricks Confluent Spark Kafka Dagster Podcast Episode Prefect Airflow 3 The Rise of the Data Engineer blog post dbt Jupyter Notebook Zapier cosmos library for dbt in Airflow Ruff Airflow Custom Operator Snowflake The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

Data Engineering Podcast

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Accelerated Computing in Modern Data Centers With Datapelago 55:36

10 weeks ago55:36

55:36

Summary In this episode of the Data Engineering Podcast Rajan Goyal, CEO and co-founder of Datapelago, talks about improving efficiencies in data processing by reimagining system architecture. Rajan explains the shift from hyperconverged to disaggregated and composable infrastructure, highlighting the importance of accelerated computing in modern data centers. He discusses the evolution from proprietary to open, composable stacks, emphasizing the role of open table formats and the need for a universal data processing engine, and outlines Datapelago's strategy to leverage existing frameworks like Spark and Trino while providing accelerated computing benefits. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Rajan Goyal about how to drastically improve efficiencies in data processing by re-imagining the system architecture Interview Introduction How did you get involved in the area of data management? Can you start by outlining the main factors that contribute to performance challenges in data lake environments? The different components of open data processing systems have evolved from different starting points with different objectives. In your experience, how has that un-planned and un-synchronized evolution of the ecosystem hindered the capabilities and adoption of open technologies? The introduction of a new cross-cutting capability (e.g. Iceberg) has typically taken a substantial amount of time to gain support across different engines and ecosystems. What do you see as the point of highest leverage to improve the capabilities of the entire stack with the least amount of co-ordination? What was the motivating insight that led you to invest in the technology that powers Datapelago? Can you describe the system design of Datapelago and how it integrates with existing data engines? The growth in the generation and application of unstructured data is a notable shift in the work being done by data teams. What are the areas of overlap in the fundamental nature of data (whether structured, semi-structured, or unstructured) that you are able to exploit to bridge the processing gap? What are the most interesting, innovative, or unexpected ways that you have seen Datapelago used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Datapelago? When is Datapelago the wrong choice? What do you have planned for the future of Datapelago? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Datapelago MIPS Architecture ARM Architecture AWS Nitro Mellanox Nvidia Von Neumann Architecture TPU == Tensor Processing Unit FPGA == Field-Programmable Gate Array Spark Trino Iceberg Podcast Episode Delta Lake Podcast Episode Hudi Podcast Episode Apache Gluten Intermediate Representation Turing Completeness LLVM Amdahl's Law LSTM == Long Short-Term Memory The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

Data Engineering Podcast

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The Future of Data Engineering: AI, LLMs, and Automation 59:39

12 weeks ago59:39

59:39

Summary In this episode of the Data Engineering Podcast Gleb Mezhanskiy, CEO and co-founder of DataFold, talks about the intersection of AI and data engineering. He discusses the challenges and opportunities of integrating AI into data engineering, particularly using large language models (LLMs) to enhance productivity and reduce manual toil. The conversation covers the potential of AI to transform data engineering tasks, such as text-to-SQL interfaces and creating semantic graphs to improve data accessibility, and explores practical applications of LLMs in automating code reviews, testing, and understanding data lineage. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Gleb Mezhanskiy about Interview Introduction How did you get involved in the area of data management? modern data stack is dead where is AI in the data stack? "buy our tool to ship AI" opportunities for LLM in DE workflow Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links Datafold Copilot Cursor IDE AI Agents DataChat AI Engineering Podcast Episode Metrics Layer Emacs LangChain LangGraph CrewAI The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

Data Engineering Podcast

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Evolving Responsibilities in AI Data Management 38:57

13 weeks ago38:57

38:57

Summary In this episode of the Data Engineering Podcast Bartosz Mikulski talks about preparing data for AI applications. Bartosz shares his journey from data engineering to MLOps and emphasizes the importance of data testing over software development in AI contexts. He discusses the types of data assets required for AI applications, including extensive test datasets, especially in generative AI, and explains the differences in data requirements for various AI application styles. The conversation also explores the skills data engineers need to transition into AI, such as familiarity with vector databases and new data modeling strategies, and highlights the challenges of evolving AI applications, including frequent reprocessing of data when changing chunking strategies or embedding models. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Bartosz Mikulski about how to prepare data for use in AI applications Interview Introduction How did you get involved in the area of data management? Can you start by outlining some of the main categories of data assets that are needed for AI applications? How does the nature of the application change those requirements? (e.g. RAG app vs. agent, etc.) How do the different assets map to the stages of the application lifecycle? What are some of the common roles and divisions of responsibility that you see in the construction and operation of a "typical" AI application? For data engineers who are used to data warehousing/BI, what are the skills that map to AI apps? What are some of the data modeling patterns that are needed to support AI apps? chunking strategies metadata management What are the new categories of data that data engineers need to manage in the context of AI applications? agent memory generation/evolution conversation history management data collection for fine tuning What are some of the notable evolutions in the space of AI applications and their patterns that have happened in the past ~1-2 years that relate to the responsibilities of data engineers? What are some of the skills gaps that teams should be aware of and identify training opportunities for? What are the most interesting, innovative, or unexpected ways that you have seen data teams address the needs of AI applications? What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI applications and their reliance on data? What are some of the emerging trends that you are paying particular attention to? Contact Info Website LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links Spark Ray Chunking Strategies Hypothetical document embeddings Model Fine Tuning Prompt Compression The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

Data Engineering Podcast

1
CSVs Will Never Die And OneSchema Is Counting On It 54:40

18 weeks ago54:40

54:40

Summary In this episode of the Data Engineering Podcast Andrew Luo, CEO of OneSchema, talks about handling CSV data in business operations. Andrew shares his background in data engineering and CRM migration, which led to the creation of OneSchema, a platform designed to automate CSV imports and improve data validation processes. He discusses the challenges of working with CSVs, including inconsistent type representation, lack of schema information, and technical complexities, and explains how OneSchema addresses these issues using multiple CSV parsers and AI for data type inference and validation. Andrew highlights the business case for OneSchema, emphasizing efficiency gains for companies dealing with large volumes of CSV data, and shares plans to expand support for other data formats and integrate AI-driven transformation packs for specific industries. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Andrew Luo about how OneSchema addresses the headaches of dealing with CSV data for your business Interview Introduction How did you get involved in the area of data management? Despite the years of evolution and improvement in data storage and interchange formats, CSVs are just as prevalent as ever. What are your opinions/theories on why they are so ubiquitous? What are some of the major sources of CSV data for teams that rely on them for business and analytical processes? The most obvious challenge with CSVs is their lack of type information, but they are notorious for having numerous other problems. What are some of the other major challenges involved with using CSVs for data interchange/ingestion? Can you describe what you are building at OneSchema and the story behind it? What are the core problems that you are solving, and for whom? Can you describe how you have architected your platform to be able to manage the variety, volume, and multi-tenancy of data that you process? How have the design and goals of the product changed since you first started working on it? What are some of the major performance issues that you have encountered while dealing with CSV data at scale? What are some of the most surprising things that you have learned about CSVs in the process of building OneSchema? What are the most interesting, innovative, or unexpected ways that you have seen OneSchema used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on OneSchema? When is OneSchema the wrong choice? What do you have planned for the future of OneSchema? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links OneSchema EDI == Electronic Data Interchange UTF-8 BOM (Byte Order Mark) Characters SOAP CSV RFC Iceberg SSIS == SQL Server Integration Services MS Access Datafusion JSON Schema SFTP == Secure File Transfer Protocol The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

Data Engineering Podcast

1
Breaking Down Data Silos: AI and ML in Master Data Management 57:30

19 weeks ago57:30

57:30

Summary In this episode of the Data Engineering Podcast Dan Bruckner, co-founder and CTO of Tamr, talks about the application of machine learning (ML) and artificial intelligence (AI) in master data management (MDM). Dan shares his journey from working at CERN to becoming a data expert and discusses the challenges of reconciling large-scale organizational data. He explains how data silos arise from independent teams and highlights the importance of combining traditional techniques with modern AI to address the nuances of data reconciliation. Dan emphasizes the transformative potential of large language models (LLMs) in creating more natural user experiences, improving trust in AI-driven data solutions, and simplifying complex data management processes. He also discusses the balance between using AI for complex data problems and the necessity of human oversight to ensure accuracy and trust. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. As a listener of the Data Engineering Podcast you clearly care about data and how it affects your organization and the world. For even more perspective on the ways that data impacts everything around us don't miss Data Citizens® Dialogues, the forward-thinking podcast brought to you by Collibra. You'll get further insights from industry leaders, innovators, and executives in the world's largest companies on the topics that are top of mind for everyone. In every episode of Data Citizens® Dialogues, industry leaders unpack data’s impact on the world; like in their episode “The Secret Sauce Behind McDonald’s Data Strategy”, which digs into how AI-driven tools can be used to support crew efficiency and customer interactions. In particular I appreciate the ability to hear about the challenges that enterprise scale businesses are tackling in this fast-moving field. The Data Citizens Dialogues podcast is bringing the data conversation to you, so start listening now! Follow Data Citizens Dialogues on Apple, Spotify, YouTube, or wherever you get your podcasts. Your host is Tobias Macey and today I'm interviewing Dan Bruckner about the application of ML and AI techniques to the challenge of reconciling data at the scale of business Interview Introduction How did you get involved in the area of data management? Can you start by giving an overview of the different ways that organizational data becomes unwieldy and needs to be consolidated and reconciled? How does that reconciliation relate to the practice of "master data management" What are the scaling challenges with the current set of practices for reconciling data? ML has been applied to data cleaning for a long time in the form of entity resolution, etc. How has the landscape evolved or matured in recent years? What (if any) transformative capabilities do LLMs introduce? What are the missing pieces/improvements that are necessary to make current AI systems usable out-of-the-box for data cleaning? What are the strategic decisions that need to be addressed when implementing ML/AI techniques in the data cleaning/reconciliation process? What are the risks involved in bringing ML to bear on data cleaning for inexperienced teams? What are the most interesting, innovative, or unexpected ways that you have seen ML techniques used in data resolution? What are the most interesting, unexpected, or challenging lessons that you have learned while working on using ML/AI in master data management? When is ML/AI the wrong choice for data cleaning/reconciliation? What are your hopes/predictions for the future of ML/AI applications in MDM and data cleaning? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links Tamr Master Data Management CERN LHC Michael Stonebraker Conway's Law Expert Systems Information Retrieval Active Learning The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

Data Engineering Podcast

1
Building a Data Vision Board: A Guide to Strategic Planning 49:59

21 weeks ago49:59

49:59

Summary In this episode of the Data Engineering Podcast Lior Barak shares his insights on developing a three-year strategic vision for data management. He discusses the importance of having a strategic plan for data, highlighting the need for data teams to focus on impact rather than just enablement. He introduces the concept of a "data vision board" and explains how it can help organizations outline their strategic vision by considering three key forces: regulation, stakeholders, and organizational goals. Lior emphasizes the importance of balancing short-term pressures with long-term strategic goals, quantifying the cost of data issues to prioritize effectively, and maintaining the strategic vision as a living document through regular reviews. He encourages data teams to shift from being enablers to impact creators and provides practical advice on implementing a data vision board, setting clear KPIs, and embracing a product mindset to create tangible business impacts through strategic data management. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management It’s 2024, why are we still doing data migrations by hand? Teams spend months—sometimes years—manually converting queries and validating data, burning resources and crushing morale. Datafold's AI-powered Migration Agent brings migrations into the modern era. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today to learn how Datafold can automate your migration and ensure source to target parity. Your host is Tobias Macey and today I'm interviewing Lior Barak about how to develop your three year strategic vision for data Interview Introduction How did you get involved in the area of data management? Can you start by giving an outline of the types of problems that occur as a result of not developing a strategic plan for an organization's data systems? What is the format that you recommend for capturing that strategic vision? What are the types of decisions and details that you believe should be included in a vision statement? Why is a 3 year horizon beneficial? What does that scale of time encourage/discourage in the debate and decision-making process? Who are the personas that should be included in the process of developing this strategy document? Can you walk us through the steps and processes involved in developing the data vision board for an organization? What are the time-frames or milestones that should lead to revisiting and revising the strategic objectives? What are the most interesting, innovative, or unexpected ways that you have seen a data vision strategy used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data strategy development? When is a data vision board the wrong choice? What are some additional resources or practices that you recommend teams invest in as a supplement to this strategic vision exercise? Contact Info LinkedIn Substack Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links Vision Board Overview Episode 397: Defining A Strategy For Your Data Products Minto Pyramid Principle KPI == Key Performance Indicator OKR == Objectives and Key Results Phil Jackson: Eleven Rings (affiliate link) The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA…

Data Engineering Podcast

1
How Orchestration Impacts Data Platform Architecture 59:39

22 weeks ago59:39