Summary Data integration from source systems to their downstream destinations is the foundational step for any data product. With the increasing expecation for information to be instantly accessible, it drives the need for reliable change data capture. The team at Fivetran have recently introduced that functionality to power real-time data products. In this episode Mark Van de Wiel explains how they integrated CDC functionality into their existing product, discusses the nuances of different appr...
Sep 26, 2022•50 min•Ep. 328
Summary Regardless of how data is being used, it is critical that the information is trusted. The practice of data reliability engineering has gained momentum recently to address that question. To help support the efforts of data teams the folks at Soda Data created the Soda Checks Language and the corresponding Soda Core utility that acts on this new DSL. In this episode Tom Baeyens explains their reasons for creating a new syntax for expressing and validating checks for data assets and process...
Sep 26, 2022•41 min•Ep. 327
Summary There is a constant tension in business data between growing siloes, and breaking them down. Even when a tool is designed to integrate information as a guard against data isolation, it can easily become a silo of its own, where you have to make a point of using it to seek out information. In order to help distribute critical context about data assets and their status into the locations where work is being done Nicholas Freund co-founded Workstream. In this episode he discusses the challe...
Sep 19, 2022•55 min•Ep. 326
Summary In order to improve efficiency in any business you must first know what is contributing to wasted effort or missed opportunities. When your business operates across multiple locations it becomes even more challenging and important to gain insights into how work is being done. In this episode Tommy Yionoulis shares his experiences working in the service and hospitality industries and how that led him to found OpsAnalitica, a platform for collecting and analyzing metrics on multi location ...
Sep 19, 2022•1 hr 32 min•Ep. 325
Summary Data engineering systems are complex and interconnected with myriad and often opaque chains of dependencies. As they scale, the problems of visibility and dependency management can increase at an exponential rate. In order to turn this into a tractable problem one approach is to define and enforce contracts between producers and consumers of data. Ananth Packildurai created Schemata as a way to make the creation of schema contracts a lightweight process, allowing the dependency chains to...
Sep 12, 2022•1 hr•Ep. 324
Summary Any business that wants to understand their operations and customers through data requires some form of pipeline. Building reliable data pipelines is a complex and costly undertaking with many layered requirements. In order to reduce the amount of time and effort required to build pipelines that power critical insights Manish Jethani co-founded Hevo Data. In this episode he shares his journey from building a consumer product to launching a data pipeline service and how his frustrations a...
Sep 12, 2022•57 min•Ep. 323
Summary The global climate impacts everyone, and the rate of change introduces many questions that businesses need to consider. Getting answers to those questions is challenging, because the climate is a multidimensional and constantly evolving system. Sust Global was created to provide curated data sets for organizations to be able to analyze climate information in the context of their business needs. In this episode Gopal Erinjippurath discusses the data engineering challenges of building and ...
Sep 05, 2022•54 min•Ep. 322
Summary Data observability is a product category that has seen massive growth and adoption in recent years. Monte Carlo is in the vanguard of companies who have been enabling data teams to observe and understand their complex data systems. In this episode founders Barr Moses and Lior Gavish rejoin the show to reflect on the evolution and adoption of data observability technologies and the capabilities that are being introduced as the broader ecosystem adopts the practices. Announcements Hello an...
Sep 05, 2022•59 min•Ep. 321
Summary The dream of every engineer is to automate all of their tasks. For data engineers, this is a monumental undertaking. Orchestration engines are one step in that direction, but they are not a complete solution. In this episode Sean Knapp shares his views on what constitutes proper automation and the work that he and his team at Ascend are doing to help make it a reality. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready...
Aug 29, 2022•1 hr 4 min•Ep. 320
Summary AirBnB pioneered a number of the organizational practices that have become the goal of modern data teams. Out of that culture a number of successful businesses were created to provide the tools and methods to a broader audience. In this episode several almuni of AirBnB’s formative years who have gone on to found their own companies join the show to reflect on their shared successes, missed opportunities, and lessons learned. Announcements Hello and welcome to the Data Engineering Podcast...
Aug 28, 2022•1 hr 10 min•Ep. 319
Summary The position of Chief Data Officer (CDO) is relatively new in the business world and has not been universally adopted. As a result, not everyone understands what the responsibilities of the role are, when you need one, and how to hire for it. In this episode Tracy Daniels, CDO of Truist, shares her journey into the position, her responsibilities, and her relationship to the data professionals in her organization. Announcements Hello and welcome to the Data Engineering Podcast, the show a...
Aug 22, 2022•47 min•Ep. 318
Summary Data has permeated every aspect of our lives and the products that we interact with. As a result, end users and customers have come to expect interactions and updates with services and analytics to be fast and up to date. In this episode Shruti Bhat gives her view on the state of the ecosystem for real-time data and the work that she and her team at Rockset is doing to make it easier for engineers to build those experiences. Announcements Hello and welcome to the Data Engineering Podcast...
Aug 22, 2022•1 hr 6 min•Ep. 317
Summary Data engineers have typically left the process of data labeling to data scientists or other roles because of its nature as a manual and process heavy undertaking, focusing instead on building automation and repeatable systems. Watchful is a platform to make labeling a repeatable and scalable process that relies on codifying domain expertise. In this episode founder Shayan Mohanty explains how he and his team are bringing software best practices and automation to the world of machine lear...
Aug 14, 2022•1 hr 20 min•Ep. 316
Summary Data is useless if it isn’t being used, and you can’t use it if you don’t know where it is. Data catalogs were the first solution to this problem, but they are only helpful if you know what you are looking for. In this episode Shinji Kim discusses the challenges of data discovery and how to collect and preserve additional context about each piece of information so that you can find what you need when you don’t even know what you’re looking for yet. Announcements Hello and welcome to the ...
Aug 14, 2022•53 min•Ep. 315
Summary Data mesh is a frequent topic of conversation in the data community, with many debates about how and when to employ this architectural pattern. The team at AgileLab have first-hand experience helping large enterprise organizations evaluate and implement their own data mesh strategies. In this episode Paolo Platter shares the lessons they have learned in that process, the Data Mesh Boost platform that they have built to reduce some of the boilerplate required to make it successful, and so...
Aug 06, 2022•49 min•Ep. 314
Summary The optimal format for storage and retrieval of data is dependent on how it is going to be used. For analytical systems there are decades of investment in data warehouses and various modeling techniques. For machine learning applications relational models require additional processing to be directly useful, which is why there has been a growth in the use of vector databases. These platforms store direct representations of the vector embeddings that machine learning models rely on for com...
Aug 06, 2022•59 min•Ep. 313
Summary Exploratory data analysis works best when the feedback loop is fast and iterative. This is easy to achieve when you are working on small datasets, but as they scale up beyond what can fit on a single machine those short iterations quickly become long and tedious. The Arkouda project is a Python interface built on top of the Chapel compiler to bring back those interactive speeds for exploratory analysis on horizontally scalable compute that parallelizes operations on large volumes of data...
Jul 31, 2022•41 min•Ep. 312
Summary Data lineage is the roadmap for your data platform, providing visibility into all of the dependencies for any report, machine learning model, or data warehouse table that you are working with. Because of its centrality to your data systems it is valuable for debugging, governance, understanding context, and myriad other purposes. This means that it is important to have an accurate and complete lineage graph so that you don’t have to perform your own detective work when time is in short s...
Jul 31, 2022•1 hr 5 min•Ep. 311
Summary Data engineering is a difficult job, requiring a large number of skills that often don’t overlap. Any effort to understand how to start a career in the role has required stitching together information from a multitude of resources that might not all agree with each other. In order to provide a single reference for anyone tasked with data engineering responsibilities Joe Reis and Matt Housley took it upon themselves to write the book "Fundamentals of Data Engineering". In this episode the...
Jul 24, 2022•1 hr 1 min•Ep. 309
Summary The current stage of evolution in the data management ecosystem has resulted in domain and use case specific orchestration capabilities being incorporated into various tools. This complicates the work involved in making end-to-end workflows visible and integrated. Dagster has invested in bringing insights about external tools’ dependency graphs into one place through its "software defined assets" functionality. In this episode Nick Schrock discusses the importance of orchestration and a ...
Jul 24, 2022•58 min•Ep. 310
Summary There are extensive and valuable data sets that are available outside the bounds of your organization. Whether that data is public, paid, or scraped it requires investment and upkeep to acquire and integrate it with your systems. Crux was built to reduce the total cost of acquisition and ownership for integrating external data, offering a fully managed service for delivering those data assets in the manner that best suits your infrastructure. In this episode Crux CTO Mark Etherington dis...
Jul 17, 2022•1 hr 7 min•Ep. 307
Summary Data engineering is a large and growing subject, with new technologies, specializations, and "best practices" emerging at an accelerating pace. This podcast does its best to explore this fractal ecosystem, and has been at it for the past 5+ years. In this episode Joe Reis, founder of Ternary Data and co-author of "Fundamentals of Data Engineering", turns the tables and interviews the host, Tobias Macey, about his journey into podcasting, how he runs the show behind the scenes, and the ot...
Jul 17, 2022•57 min•Ep. 308
Summary Building a data platform is a journey, not a destination. Beyond the work of assembling a set of technologies and building integrations across them, there is also the work of growing and organizing a team that can support and benefit from that platform. In this episode Inbar Yogev and Lior Winner share the journey that they and their teams at Riskified have been on for their data platform. They also discuss how they have established a guild system for training and supporting data profess...
Jul 10, 2022•40 min•Ep. 306
Summary Building and maintaining reliable data assets is the prime directive for data engineers. While it is easy to say, it is endlessly complex to implement, requiring data professionals to be experts in a wide range of disparate topics while designing and implementing complex topologies of information workflows. In order to make this a tractable problem it is essential that engineers embrace automation at every opportunity. In this episode Chris Riccomini shares his experiences building and s...
Jul 10, 2022•1 hr 5 min•Ep. 305
Summary The perennial challenge of data engineers is ensuring that information is integrated reliably. While it is straightforward to know whether a synchronization process succeeded, it is not always clear whether every record was copied correctly. In order to quickly identify if and how two data systems are out of sync Gleb Mezhanskiy and Simon Eskildsen partnered to create the open source data-diff utility. In this episode they explain how the utility is implemented to run quickly and how you...
Jul 03, 2022•1 hr 11 min•Ep. 304
Summary The ecosystem for data tools has been going through rapid and constant evolution over the past several years. These technological shifts have brought about corresponding changes in data and platform architectures for managing data and analytical workflows. In this episode Colleen Tartow shares her insights into the motivating factors and benefits of the most prominent patterns that are in the popular narrative; data mesh and the modern data stack. She also discusses her views on the role...
Jul 03, 2022•59 min•Ep. 303
Summary The proliferation of sensors and GPS devices has dramatically increased the number of applications for spatial data, and the need for scalable geospatial analytics. In order to reduce the friction involved in aggregating disparate data sets that share geographic similarities the Unfolded team built a platform that supports working across raster, vector, and tabular data in a single system. In this episode Isaac Brodsky explains how the Unfolded platform is architected, their experience j...
Jun 27, 2022•1 hr 7 min•Ep. 301
Summary The most complicated part of data engineering is the effort involved in making the raw data fit into the narrative of the business. Master Data Management (MDM) is the process of building consensus around what the information actually means in the context of the business and then shaping the data to match those semantics. In this episode Malcolm Hawker shares his years of experience working in this domain to explore the combination of technical and social skills that are necessary to mak...
Jun 27, 2022•1 hr 9 min•Ep. 302
Summary Data analysis is a valuable exercise that is often out of reach of non-technical users as a result of the complexity of data systems. In order to lower the barrier to entry Ryan Buick created the Canvas application with a spreadsheet oriented workflow that is understandable to a wide audience. In this episode Ryan explains how he and his team have designed their platform to bring everyone onto a level playing field and the benefits that it provides to the organization. Announcements Hell...
Jun 19, 2022•43 min•Ep. 300
Summary Metadata is the lifeblood of your data platform, providing information about what is happening in your systems. A variety of platforms have been developed to capture and analyze that information to great effect, but they are inherently limited in their utility due to their nature as storage systems. In order to level up their value a new trend of active metadata is being implemented, allowing use cases like keeping BI reports up to date, auto-scaling your warehouses, and automated data g...
Jun 19, 2022•53 min•Ep. 299
