Data Engineering Podcast

In this episode, I had the pleasure of speaking with Ken Pickering, VP of Engineering at Going, about the intricacies of streaming data into a Trino and Iceberg lakehouse. Ken shared his journey from product engineering to becoming deeply involved in data-centric roles, highlighting his experiences in ecommerce and InsurTech. At Going, Ken leads the data platform team, focusing on finding travel deals for consumers, a task that involves handling massive volumes of flight data and event stream information.

Ken explained the dual approach of passive and active search strategies used by Going to manage the vast data landscape. Passive search involves aggregating data from global distribution systems, while active search is more transactional, querying specific flight prices. This approach helps Going sift through approximately 50 petabytes of data annually to identify the best travel deals.

We delved into the technical architecture supporting these operations, including the use of Confluent for data streaming, Starburst Galaxy for transformation, and Databricks for modeling. Ken emphasized the importance of an open lakehouse architecture, which allows for flexibility and scalability as the business grows.

Ken also discussed the composition of Going's engineering and data teams, highlighting the collaborative nature of their work and the reliance on vendor tooling to streamline operations. He shared insights into the challenges and strategies of managing data life cycles, ensuring data quality, and maintaining uptime for consumer-facing applications.

Throughout our conversation, Ken provided a glimpse into the future of Going's data architecture, including potential expansions into other travel modes and the integration of large language models for enhanced customer interaction. This episode offers a comprehensive look at the complexities and innovations in building a data-driven travel advisory service.

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
• Imagine catching data issues before they snowball into bigger problems. That’s what Datafold’s new Monitors do. With automatic monitoring for cross-database data diffs, schema changes, key metrics, and custom data tests, you can catch discrepancies and anomalies in real time, right at the source. Whether it’s maintaining data integrity or preventing costly mistakes, Datafold Monitors give you the visibility and control you need to keep your entire data stack running smoothly. Want to stop issues before they hit production? Learn more at dataengineeringpodcast.com/datafold today!
• Your host is Tobias Macey and today I'm interviewing Burak Karakan about the benefits of building code-only data systems

• Introduction
• How did you get involved in the area of data management?
• Can you describe what Bruin is and the story behind it?
  • Who is your target audience?
• There are numerous tools that address the ETL workflow for analytical data. What are the pain points that you are focused on for your target users?
• How does a code-only approach to data pipelines help in addressing the pain points of analytical workflows?
  • How might it act as a limiting factor for organizational involvement?
• Can you describe how Bruin is designed?
  • How have the design and scope of Bruin evolved since you first started working on it?
• You call out the ability to mix SQL and Python for transformation pipelines. What are the components that allow for that functionality?
  • What are some of the ways that the combination of Python and SQL improves ergonomics of transformation workflows?
• What are the key features of Bruin that help to streamline the efforts of organizations building analytical systems?
• Can you describe the workflow of someone going from source data to warehouse and dashboard using Bruin and Ingestr?
• What are the opportunities for contributions to Bruin and Ingestr to expand their capabilities?
• What are the most interesting, innovative, or unexpected ways that you have seen Bruin and Ingestr used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Bruin?
• When is Bruin the wrong choice?
• What do you have planned for the future of Bruin?

• LinkedIn

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

• 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 [email protected] with your story.

• Bruin
• Fivetran
• Stitch
• Ingestr
• Bruin CLI
• Meltano
• SQLGlot
• dbt
• SQLMesh
  • Podcast Episode
• SDF
  • Podcast Episode
• Airflow
• Dagster
• Snowpark
• Atlan
• Evidence

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
• Imagine catching data issues before they snowball into bigger problems. That’s what Datafold’s new Monitors do. With automatic monitoring for cross-database data diffs, schema changes, key metrics, and custom data tests, you can catch discrepancies and anomalies in real time, right at the source. Whether it’s maintaining data integrity or preventing costly mistakes, Datafold Monitors give you the visibility and control you need to keep your entire data stack running smoothly. Want to stop issues before they hit production? Learn more at dataengineeringpodcast.com/datafold today!
• 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 you should listen to Data Citizens® Dialogues, the forward-thinking podcast from the folks at 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. They address questions around AI governance, data sharing, and working at global scale. In particular I appreciate the ability to hear about the challenges that enterprise scale businesses are tackling in this fast-moving field. While data is shaping our world, Data Citizens Dialogues is shaping the conversation. Subscribe to Data Citizens Dialogues on Apple, Spotify, Youtube, or wherever you get your podcasts.
• Your host is Tobias Macey and today I'm interviewing Leonid Ryzhyk, Lalith Suresh, and Mihai Budiu about Feldera, an incremental compute engine for continous computation of data, ML, and AI workloads

• Introduction
• Can you describe what Feldera is and the story behind it?
• DBSP (the theory behind Feldera) has won multiple awards from the database research community. Can you explain what it is and how it solves the incremental computation problem?
• Depending on which angle you look at it, Feldera has attributes of data warehouses, federated query engines, and stream processors. What are the unique use cases that Feldera is designed to address?
  • In what situations would you replace another technology with Feldera?
  • When is it an additive technology?
• Can you describe the architecture of Feldera?
  • How have the design and scope evolved since you first started working on it?
• What are the state storage interfaces available in Feldera?
  • What are the opportunities for integrating with or building on top of open table formats like Iceberg, Lance, Hudi, etc.?
• Can you describe a typical workflow for an engineer building with Feldera?
• You advertise Feldera's utility in ML and AI use cases in addition to data management. What are the features that make it conducive to those applications?
• What is your philosophy toward the community growth and engagement with the open source aspects of Feldera and how you're balancing that with sustainability of the project and business?
• What are the most interesting, innovative, or unexpected ways that you have seen Feldera used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Feldera?
• When is Feldera the wrong choice?
• What do you have planned for the future of Feldera?

• Leonid
  • Website
  • GitHub
  • LinkedIn
• Lalith
  • LinkedIn
  • Website
• Mihai
  • Website
  • GitHub

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

• 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 [email protected] with your story.

• Feldera
  • GitHub
• DBSP paper
  • Rust Crate
• Differential Dataflow
• Trino
• Flink
• Spark
• Materialize
• Clickhouse
  • Podcast Episode
• DuckDB
  • Podcast Episode
• Snowflake
• Arrow
• Substrait
• DataFusion
• DSP == Digital Signal Processing
• CDC == Change Data Capture
• PRQL
• LSM (Log-Structured Merge) Tree
• Iceberg
  • Podcast Episode
• Delta Lake
  • Podcast Episode
• Open VSwitch
• Feature Engineering
• Calcite

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
• Imagine catching data issues before they snowball into bigger problems. That’s what Datafold’s new Monitors do. With automatic monitoring for cross-database data diffs, schema changes, key metrics, and custom data tests, you can catch discrepancies and anomalies in real time, right at the source. Whether it’s maintaining data integrity or preventing costly mistakes, Datafold Monitors give you the visibility and control you need to keep your entire data stack running smoothly. Want to stop issues before they hit production? Learn more at dataengineeringpodcast.com/datafold today!
• Your host is Tobias Macey and today I'm welcoming back Gleb Mezhanskiy to talk about Datafold's experience bringing AI to bear on the problem of migrating your data stack

• Introduction
• How did you get involved in the area of data management?
• Can you describe what the Data Migration Agent is and the story behind it?
  • What is the core problem that you are targeting with the agent?
• What are the biggest time sinks in the process of database and tooling migration that teams run into?
• Can you describe the architecture of your agent?
  • What was your selection and evaluation process for the LLM that you are using?
• What were some of the main unknowns that you had to discover going into the project?
  • What are some of the evolutions in the ecosystem that occurred either during the development process or since your initial launch that have caused you to second-guess elements of the design?
• In terms of SQL translation there are libraries such as SQLGlot and the work being done with SDF that aim to address that through AST parsing and subsequent dialect generation. What are the ways that approach is insufficient in the context of a platform migration?
• How does the approach you are taking with the combination of data-diffing and automated translation help build confidence in the migration target?
• What are the most interesting, innovative, or unexpected ways that you have seen the Data Migration Agent used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on building an AI powered migration assistant?
• When is the data migration agent the wrong choice?
• What do you have planned for the future of applications of AI at Datafold?

• LinkedIn

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

• 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 [email protected] with your story.

• Datafold
• Datafold Migration Agent
• Datafold data-diff
• Datafold Reconciliation Podcast Episode
• SQLGlot
• Lark parser
• Claude 3.5 Sonnet
• Looker
  • Podcast Episode

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
• Imagine catching data issues before they snowball into bigger problems. That’s what Datafold’s new Monitors do. With automatic monitoring for cross-database data diffs, schema changes, key metrics, and custom data tests, you can catch discrepancies and anomalies in real time, right at the source. Whether it’s maintaining data integrity or preventing costly mistakes, Datafold Monitors give you the visibility and control you need to keep your entire data stack running smoothly. Want to stop issues before they hit production? Learn more at dataengineeringpodcast.com/datafold today!
• Your host is Tobias Macey and today I'm interviewing Weston Pace about the Lance file and table format for column-oriented vector storage

• Introduction
• How did you get involved in the area of data management?
• Can you describe what Lance is and the story behind it?
  • What are the core problems that Lance is designed to solve?
    • What is explicitly out of scope?
• The README mentions that it is straightforward to convert to Lance from Parquet. What is the motivation for this compatibility/conversion support?
  • What formats does Lance replace or obviate?
• In terms of data modeling Lance obviously adds a vector type, what are the features and constraints that engineers should be aware of when modeling their embeddings or arbitrary vectors?
  • Are there any practical or hard limitations on vector dimensionality?
• When generating Lance files/datasets, what are some considerations to be aware of for balancing file/chunk sizes for I/O efficiency and random access in cloud storage?
• I noticed that the file specification has space for feature flags. How has that aided in enabling experimentation in new capabilities and optimizations?
• What are some of the engineering and design decisions that were most challenging and/or had the biggest impact on the performance and utility of Lance?
• The most obvious interface for reading and writing Lance files is through LanceDB. Can you describe the use cases that it focuses on and its notable features?
  • What are the other main integrations for Lance?
  • What are the opportunities or roadblocks in adding support for Lance and vector storage/indexes in e.g. Iceberg or Delta to enable its use in data lake environments?
• What are the most interesting, innovative, or unexpected ways that you have seen Lance used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on the Lance format?
• When is Lance the wrong choice?
• What do you have planned for the future of Lance?

• LinkedIn
• GitHub

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

• Lance Format
• LanceDB
• Substrait
• PyArrow
• FAISS
• Pinecone
  • Podcast Episode
• Parquet
• Iceberg
  • Podcast Episode
• Delta Lake
  • Podcast Episode
• PyLance
• Hilbert Curves
• SIFT Vectors
• S3 Express
• Weka
• DataFusion
• Ray Data
• Torch Data Loader
• HNSW == Hierarchical Navigable Small Worlds vector index
• IVFPQ vector index
• GeoJSON
• Polars

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
• Imagine catching data issues before they snowball into bigger problems. That’s what Datafold’s new Monitors do. With automatic monitoring for cross-database data diffs, schema changes, key metrics, and custom data tests, you can catch discrepancies and anomalies in real time, right at the source. Whether it’s maintaining data integrity or preventing costly mistakes, Datafold Monitors give you the visibility and control you need to keep your entire data stack running smoothly. Want to stop issues before they hit production? Learn more at dataengineeringpodcast.com/datafold today!
• Your host is Tobias Macey and today I'm interviewing Adrian Brudaru and Marcin Rudolf, cofounders at dltHub, about the growth of dlt and the numerous ways that you can use it to address the complexities of data integration

• Introduction
• How did you get involved in the area of data management?
• Can you describe what dlt is and how it has evolved since we last spoke (September 2023)?
  • What are the core principles that guide your work on dlt and dlthub?
• You have taken a very opinionated stance against managed extract/load services. What are the shortcomings of those platforms, and when would you argue in their favor?
• The landscape of data movement has undergone some interesting changes over the past year. Most notably, the growth of PyAirbyte and the rapid shifts around the needs of generative AI stacks (vector stores, unstructured data processing, etc.). How has that informed your product development and positioning?
  • The Python ecosystem, and in particular data-oriented Python, has also undergone substantial evolution. What are the developments in the libraries and frameworks that you have been able to benefit from?
• What are some of the notable investments that you have made in the developer experience for building dlt pipelines?
  • How have the interfaces for source/destination development improved?
• You recently published a post about the idea of a portable data lake. What are the missing pieces that would make that possible, and what are the developments/technologies that put that idea within reach?
• What is your strategy for building a sustainable product on top of dlt?
  • How does that strategy help to form a "virtuous cycle" of improving the open source foundation?
• What are the most interesting, innovative, or unexpected ways that you have seen dlt used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on dlt?
• When is dlt the wrong choice?
• What do you have planned for the future of dlt/dlthub?

• Adrian
  • LinkedIn
• Marcin
  • LinkedIn

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

• 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 [email protected] with your story.

• dlt
  • Podcast Episode
• PyArrow
• Polars
• Ibis
• DuckDB
  • Podcast Episode
• dlt Data Contracts
• RAG == Retrieval Augmented Generation
  • AI Engineering Podcast Episode
• PyAirbyte
• OpenAI o1 Model
• LanceDB
• QDrant Embedded
• Airflow
• GitHub Actions
• Arrow DataFusion
• Apache Arrow
• PyIceberg
• Delta-RS
• SCD2 == Slowly Changing Dimensions
• SQLAlchemy
• SQLGlot
• FSSpec
• Pydantic
• Spacy
• Entity Recognition
• Parquet File Format
• Python Decorator
• REST API Toolkit
• OpenAPI Connector Generator
• ConnectorX
• Python no-GIL
• Delta Lake
  • Podcast Episode
• SQLMesh
  • Podcast Episode
• Hamilton
• Tabular
• PostHog
  • Podcast.__init__ Episode
• AsyncIO
• Cursor.AI
• Data Mesh
  • Podcast Episode
• FastAPI
• LangChain
• GraphRAG
  • AI Engineering Podcast Episode
• Property Graph
• Python uv

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
• Imagine catching data issues before they snowball into bigger problems. That’s what Datafold’s new Monitors do. With automatic monitoring for cross-database data diffs, schema changes, key metrics, and custom data tests, you can catch discrepancies and anomalies in real time, right at the source. Whether it’s maintaining data integrity or preventing costly mistakes, Datafold Monitors give you the visibility and control you need to keep your entire data stack running smoothly. Want to stop issues before they hit production? Learn more at dataengineeringpodcast.com/datafold today!
• Your host is Tobias Macey and today I'm interviewing Lukas Schulte about SDF, a fast and expressive SQL transformation tool that understands your schema

• Introduction
• How did you get involved in the area of data management?
• Can you describe what SDF is and the story behind it?
  • What's the story behind the name?
• What problem are you solving with SDF?
  • dbt has been the dominant player for SQL-based transformations for several years, with other notable competition in the form of SQLMesh. Can you give an overview of the venn diagram for features and functionality across SDF, dbt and SQLMesh?
• Can you describe the design and implementation of SDF?
  • How have the scope and goals of the project changed since you first started working on it?
• What does the development experience look like for a team working with SDF?
  • How does that differ between the open and paid versions of the product?
• What are the features and functionality that SDF offers to address intra- and inter-team collaboration?
• One of the challenges for any second-mover technology with an established competitor is the adoption/migration path for teams who have already invested in the incumbent (dbt in this case). How are you addressing that barrier for SDF?
  • Beyond the core migration path of the direct functionality of the incumbent product is the amount of tooling and communal knowledge that grows up around that product. How are you thinking about that aspect of the current landscape?
• What is your governing principle for what capabilities are in the open core and which go in the paid product?
• What are the most interesting, innovative, or unexpected ways that you have seen SDF used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on SDF?
• When is SDF the wrong choice?
• What do you have planned for the future of SDF?

• LinkedIn

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

• SDF
• Semantic Data Warehouse
• asdf-vm
• dbt
• Software Linting)
• SQLMesh
  • Podcast Episode
• Coalesce
  • Podcast Episode
• Apache Iceberg
  • Podcast Episode
• DuckDB 
  • Podcast Episode 
• SDF Classifiers
• dbt Semantic Layer
• dbt expectations
• Apache Datafusion
• Ibis

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
• Your host is Tobias Macey and today I'm interviewing Michel Tricot about the journey to the 1.0 launch of Airbyte and what that means for the project

• Introduction
• How did you get involved in the area of data management?
• Can you describe what Airbyte is and the story behind it?
• What are some of the notable milestones that you have traversed on your path to the 1.0 release?
• The ecosystem has gone through some significant shifts since you first launched Airbyte. How have trends such as generative AI, the rise and fall of the "modern data stack", and the shifts in investment impacted your overall product and business strategies?
• What are some of the hard-won lessons that you have learned about the realities of data movement and integration?
  • What are some of the most interesting/challenging/surprising edge cases or performance bottlenecks that you have had to address?
• What are the core architectural decisions that have proven to be effective?
  • How has the architecture had to change as you progressed to the 1.0 release?
• A 1.0 version signals a degree of stability and commitment. Can you describe the decision process that you went through in committing to a 1.0 version?
• What are the most interesting, innovative, or unexpected ways that you have seen Airbyte used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Airbyte?
• When is Airbyte the wrong choice?
• What do you have planned for the future of Airbyte after the 1.0 launch?

• LinkedIn

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

• 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 [email protected] with your story.

• Airbyte
  • Podcast Episode
• Airbyte Cloud
• Airbyte Connector Builder
• Singer Protocol
• Airbyte Protocol
• Airbyte CDK
• Modern Data Stack
• ELT
• Vector Database
• dbt
• Fivetran
  • Podcast Episode
• Meltano
  • Podcast Episode
• dlt
• Reverse ETL
• GraphRAG
  • AI Engineering Podcast Episode

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
• Your host is Tobias Macey and today I'm interviewing Junping Du about Gravitino, an open source metadata service for a unified view of all of your schemas

• Introduction
• How did you get involved in the area of data management?
• Can you describe what Gravitino is and the story behind it?
• What problems are you solving with Gravitino?
  • What are the methods that teams have relied on in the absence of Gravitino to address those use cases?
• What led to the Hive Metastore being the default for so long?
  • What are the opportunities for innovation and new functionality in the metadata service?
• The documentation suggests that Gravitino has overlap with a number of tool categories such as table schema (Hive metastore), metadata repository (Open Metadata), data federation (Trino/Alluxio). What are the capabilities that it can completely replace, and which will require other systems for more comprehensive functionality?
• What are the capabilities that you are explicitly keeping out of scope for Gravitino?
• Can you describe the technical architecture of Gravitino?
  • How have the design and scope evolved from when you first started working on it?
• Can you describe how Gravitino integrates into an overall data platform?
  • In a typical day, what are the different ways that a data engineer or data analyst might interact with Gravitino?
• One of the features that you highlight is centralized permissions management. Can you describe the access control model that you use for unifying across underlying sources?
• What are the most interesting, innovative, or unexpected ways that you have seen Gravitino used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Gravitino?
• When is Gravitino the wrong choice?
• What do you have planned for the future of Gravitino?

• LinkedIn
• GitHub

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

• 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 [email protected] with your story.

• Gravitino
• Hadoop
• Datastrato
• PyTorch
• Ray
• Data Fabric
• Hive
• Iceberg
  • Podcast Episode
• Hive Metastore
• Trino
• OpenMetadata
  • Podcast Episode
• Alluxio
• Atlan
  • Podcast Episode
• Spark
• Thrift

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
• Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
• Your host is Tobias Macey and today I'm interviewing Chris Bergh about his tireless quest to simplify the lives of data engineers

• Introduction
• How did you get involved in the area of data management?
• Can you describe what DataKitchen is and the story behind it?
• You helped to define and popularize "DataOps", which then went through a journey of misappropriation similar to "DevOps", and has since faded in use. What is your view on the realities of "DataOps" today?
• Out of the popularized wave of "DataOps" tools came subsequent trends in data observability, data reliability engineering, etc. How have those cycles influenced the way that you think about the work that you are doing at DataKitchen?
• The data ecosystem went through a massive growth period over the past ~7 years, and we are now entering a cycle of consolidation. What are the fundamental shifts that we have gone through as an industry in the management and application of data?
• What are the challenges that never went away?
• You recently open sourced the dataops-testgen and dataops-observability tools. What are the outcomes that you are trying to produce with those projects?
• What are the areas of overlap with existing tools and what are the unique capabilities that you are offering?
• Can you talk through the technical implementation of your new obserability and quality testing platform?
• What does the onboarding and integration process look like?
• Once a team has one or both tools set up, what are the typical points of interaction that they will have over the course of their workday?
• What are the most interesting, innovative, or unexpected ways that you have seen dataops-observability/testgen used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on promoting DataOps?
• What do you have planned for the future of your work at DataKitchen?

• LinkedIn

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

• DataKitchen
• Podcast Episode
• NASA
• DataOps Manifesto
• Data Reliability Engineering
• Data Observability
• dbt
• DevOps Enterprise Summit
• Building The Data Warehouse by Bill Inmon (affiliate link)
• dataops-testgen, dataops-observability
• Free Data Quality and Data Observability Certification
• Databricks
• DORA Metrics
• DORA for data