Data Engineering Podcast

Data Engineering Podcast

Weekly deep dives on data management with the engineers and entrepreneurs who are shaping the industry

Episodes

May 7, 2021 40 min
Spark is one of the most well-known frameworks for data processing, whether for batch or streaming, ETL or ML, and at any scale. Because of its popularity it has been deployed on every kind of platform you can think of. In this episode Jean-Yves Stephan shares the work that he is doing at Data Mechanics to make it sing on Kubernetes. He explains how operating in a cloud-native context simplifies some aspects of running the system w...
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The Data industry is changing rapidly, and one of the most active areas of growth is automation of data workflows. Taking cues from the DevOps movement of the past decade data professionals are orienting around the concept of DataOps. More than just a collection of tools, there are a number of organizational and conceptual changes that a proper DataOps approach depends on. In this episode Kevin Stumpf, CTO of Tecton, Maxime Beauche...
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The reason for collecting, cleaning, and organizing data is to make it usable by the organization. One of the most common and widely used methods of access is through a business intelligence dashboard. Superset is an open source option that has been gaining popularity due to its flexibility and extensible feature set. In this episode Maxime Beauchemin discusses how data engineers can use Superset to provide self service access to d...
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Most of the time when you think about a data pipeline or ETL job what comes to mind is a purely mechanistic progression of functions that move data from point A to point B. Sometimes, however, one of those transformations is actually a full-fledged machine learning project in its own right. In this episode Tal Galfsky explains how he and the team at Cherre tackled the problem of messy data for Addresses by building a natural langua...
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"Business as usual" is changing, with more companies investing in data as a first class concern. As a result, the data team is growing and introducing more specialized roles. In this episode Josh Benamram, CEO and co-founder of Databand, describes the motivations for these emerging roles, how these positions affect the team dynamics, and the types of visibility that they need into the data platform to do their jobs effectiv...
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One of the biggest obstacles to success in delivering data products is cross-team collaboration. Part of the problem is the difference in the information that each role requires to do their job and where they expect to find it. This introduces a barrier to communication that is difficult to overcome, particularly in teams that have not reached a significant level of maturity in their data journey. In this episode Prukalpa Sankar sh...
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Data quality is on the top of everyone's mind recently, but getting it right is as challenging as ever. One of the contributing factors is the number of people who are involved in the process and the potential impact on the business if something goes wrong. In this episode Maarten Masschelein and Tom Baeyens share the work they are doing at Soda to bring everyone on board to make your data clean and reliable. They explain how t...
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The world of business is becoming increasingly dependent on information that is accurate up to the minute. For analytical systems, the only way to provide this reliably is by implementing change data capture (CDC). Unfortunately, this is a non-trivial undertaking, particularly for teams that don't have extensive experience working with streaming data and complex distributed systems. In this episode Raghu Murthy, founder and CEO...
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March 16, 2021 46 min
The team at DoorDash has a complex set of optimization challenges to deal with using data that they collect from a multi-sided marketplace. In order to handle the volume and variety of information that they use to run and improve the business the data team has to build a platform that analysts and data scientists can use in a self-service manner. In this episode the head of data platform for DoorDash, Sudhir Tonse, discusses the te...
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A majority of the time spent in data engineering is copying data between systems to make the information available for different purposes. This introduces challenges such as keeping information synchronized, managing schema evolution, building transformations to match the expectations of the destination systems. H.O. Maycotte was faced with these same challenges but at a massive scale, leading him to question if there is a better w...
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The process of building and deploying machine learning projects requires a staggering number of systems and stakeholders to work in concert. In this episode Yaron Haviv, co-founder of Iguazio, discusses the complexities inherent to the process, as well as how he has worked to democratize the technologies necessary to make machine learning operations maintainable.
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Data integration is a critical piece of every data pipeline, yet it is still far from being a solved problem. There are a number of managed platforms available, but the list of options for an open source system that supports a large variety of sources and destinations is still embarrasingly short. The team at Airbyte is adding a new entry to that list with the goal of making robust and easy to use data integration more accessible t...
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Every business aims to be data driven, but not all of them succeed in that effort. In order to be able to truly derive insights from the data that an organization collects, there are certain foundational capabilities that they need to have capacity for. In order to help more businesses build those foundations, Tarush Aggarwal created 5xData, offering collaborative workshops to assist in setting up the technical and organizational s...
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With all of the tools and services available for building a data platform it can be difficult to separate the signal from the noise. One of the best ways to get a true understanding of how a technology works in practice is to hear from people who are running it in production. In this episode Zeeshan Qureshi and Michelle Ark share their experiences using DBT to manage the data warehouse for Shopify. They explain how the structured t...
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Collecting and processing metrics for monitoring use cases is an interesting data problem. It is eminently possible to generate millions or billions of data points per second, the information needs to be propagated to a central location, processed, and analyzed in timeframes on the order of milliseconds or single-digit seconds, and the consumers of the data need to be able to query the information quickly and flexibly. As the syste...
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Businesses often need to be able to ingest data from their customers in order to power the services that they provide. For each new source that they need to integrate with it is another custom set of ETL tasks that they need to maintain. In order to reduce the friction involved in supporting new data transformations David Molot and Hassan Syyid built the Hotlue platform. In this episode they describe the data integration challenges...
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The data warehouse has become the central component of the modern data stack. Building on this pattern, the team at Hightouch have created a platform that synchronizes information about your customers out to third party systems for use by marketing and sales teams. In this episode Tejas Manohar explains the benefits of sourcing customer data from one location for all of your organization to use, the technical challenges of synchron...
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As data professionals we have a number of tools available for storing, processing, and analyzing data. We also have tools for collaborating on software and analysis, but collaborating on data is still an underserved capability. Gavin Mendel-Gleason encountered this problem first hand while working on the Sesshat databank, leading him to create TerminusDB and TerminusHub. In this episode he explains how the TerminusDB system is arch...
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As more organizations are gaining experience with data management and incorporating analytics into their decision making, their next move is to adopt machine learning. In order to make those efforts sustainable, the core capability they need is for data scientists and analysts to be able to build and deploy features in a self service manner. As a result the feature store is becoming a required piece of the data platform. To fill th...
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December 28, 2020 34 min
One of the core responsibilities of data engineers is to manage the security of the information that they process. The team at Satori has a background in cybersecurity and they are using the lessons that they learned in that field to address the challenge of access control and auditing for data governance. In this episode co-founder and CTO Yoav Cohen explains how the Satori platform provides a proxy layer for your data, the challe...
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