Summary Companies of all sizes and industries are trying to use the data that they and their customers generate to survive and thrive in the modern economy. As a result, they are relying on a constantly growing number of data sources being accessed by an increasingly varied set of users. In order to help data consumers find and understand the data is available, and help the data producers understand how to prioritize their work, SelectStar has built a data discovery platform that brings everyone...
Jul 31, 2021•51 min•Ep. 208
Summary Everyone expects data to be transmitted, processed, and updated instantly as more and more products integrate streaming data. The technology to make that possible has been around for a number of years, but the barriers to adoption have still been high due to the level of technical understanding and operational capacity that have been required to run at scale. Datastax has recently introduced a new managed offering for Pulsar workloads in the form of Astra Streaming that lowers those barr...
Jul 28, 2021•1 hr•Ep. 207
Summary Collecting and cleaning data is only useful if someone can make sense of it afterward. The latest evolution in the data ecosystem is the introduction of a dedicated metrics layer to help address the challenge of adding context and semantics to raw information. In this episode Nick Handel shares the story behind Transform, a new platform that provides a managed metrics layer for your data platform. He explains the challenges that occur when metrics are maintained across a variety of syste...
Jul 23, 2021•1 hr 1 min•Ep. 206
Summary Data quality is a concern that has been gaining attention alongside the rising importance of analytics for business success. Many solutions rely on hand-coded rules for catching known bugs, or statistical analysis of records to detect anomalies retroactively. While those are useful tools, it is far better to prevent data errors before they become an outsized issue. In this episode Gleb Mezhanskiy shares some strategies for adding quality checks at every stage of your development and depl...
Jul 20, 2021•1 hr 1 min•Ep. 205
Summary There is a wealth of tools and systems available for processing data, but the user experience of integrating them and building workflows is still lacking. This is particularly important in large and complex organizations where domain knowledge and context is paramount and there may not be access to engineers for codifying that expertise. Raj Bains founded Prophecy to address this need by creating a UI first platform for building and executing data engineering workflows that orchestrates ...
Jul 16, 2021•1 hr 13 min•Ep. 204
Summary We have been building platforms and workflows to store, process, and analyze data since the earliest days of computing. Over that time there have been countless architectures, patterns, and "best practices" to make that task manageable. With the growing popularity of cloud services a new pattern has emerged and been dubbed the "Modern Data Stack". In this episode members of the GoDataDriven team, Guillermo Sanchez, Bram Ochsendorf, and Juan Perafan, explain the combinations of services t...
Jul 13, 2021•49 min•Ep. 203
Summary Every data project, whether it’s analytics, machine learning, or AI, starts with the work of data cleaning. This is a critical step and benefits from being accessible to the domain experts. Trifacta is a platform for managing your data engineering workflow to make curating, cleaning, and preparing your information more approachable for everyone in the business. In this episode CEO Adam Wilson shares the story behind the business, discusses the myriad ways that data wrangling is performed...
Jul 09, 2021•1 hr 7 min•Ep. 202
Summary At the core of every data pipeline is an workflow manager (or several). Deploying, managing, and scaling that orchestration can consume a large fraction of a data team’s energy so it is important to pick something that provides the power and flexibility that you need. SaaSGlue is a managed service that lets you connect all of your systems, across clouds and physical infrastructure, and spanning all of your programming languages. In this episode Bart and Rich Wood explain how SaaSGlue is ...
Jul 05, 2021•56 min•Ep. 201
Summary Data integration in the form of extract and load is the critical first step of every data project. There are a large number of commercial and open source projects that offer that capability but it is still far from being a solved problem. One of the most promising community efforts is that of the Singer ecosystem, but it has been plagued by inconsistent quality and design of plugins. In this episode the members of the Meltano project share the work they are doing to improve the discovery...
Jul 03, 2021•1 hr 5 min•Ep. 200
Summary While the overall concept of timeseries data is uniform, its usage and applications are far from it. One of the most demanding applications of timeseries data is for application and server monitoring due to the problem of high cardinality. In his quest to build a generalized platform for managing timeseries Paul Dix keeps getting pulled back into the monitoring arena. In this episode he shares the history of the InfluxDB project, the business that he has helped to build around it, and th...
Jun 29, 2021•1 hr 6 min•Ep. 199
Summary Data Engineering is a broad and constantly evolving topic, which makes it difficult to teach in a concise and effective manner. Despite that, Daniel Molnar and Peter Fabian started the Pipeline Academy to do exactly that. In this episode they reflect on the lessons that they learned while teaching the first cohort of their bootcamp how to be effective data engineers. By focusing on the fundamentals, and making everyone write code, they were able to build confidence and impart the importa...
Jun 26, 2021•1 hr 11 min•Ep. 198
Summary The database is the core of any system because it holds the data that drives your entire experience. We spend countless hours designing the data model, updating engine versions, and tuning performance. But how confident are you that you have configured it to be as performant as possible, given the dozens of parameters and how they interact with each other? Andy Pavlo researches autonomous database systems, and out of that research he created OtterTune to find the optimal set of parameter...
Jun 23, 2021•58 min•Ep. 197
Summary Working with unstructured data has typically been a motivation for a data lake. The challenge is imposing enough order on the platform to make it useful. Kirk Marple has spent years working with data systems and the media industry, which inspired him to build a platform for automatically organizing your unstructured assets to make them more valuable. In this episode he shares the goals of the Unstruk Data Warehouse, how it is architected to extract asset metadata and build a searchable k...
Jun 18, 2021•41 min•Ep. 196
Summary When you build a machine learning model, the first step is always to load your data. Typically this means downloading files from object storage, or querying a database. To speed up the process, why not build the model inside the database so that you don’t have to move the information? In this episode Paige Roberts explains the benefits of pushing the machine learning processing into the database layer and the approach that Vertica has taken for their implementation. If you are looking fo...
Jun 15, 2021•1 hr 6 min•Ep. 195
Summary Google pioneered an impressive number of the architectural underpinnings of the broader big data ecosystem. Now they offer the technologies that they run internally to external users of their cloud platform. In this episode Lak Lakshmanan enumerates the variety of services that are available for building your various data processing and analytical systems. He shares some of the common patterns for building pipelines to power business intelligence dashboards, machine learning applications...
Jun 12, 2021•53 min•Ep. 194
Summary The way to build maintainable software and systems is through composition of individual pieces. By making those pieces high quality and flexible they can be used in surprising ways that the original creators couldn’t have imagined. One such component that has gone above and beyond its originally envisioned use case is BookKeeper, a distributed storage system that is optimized for durability and speed. In this episode Matteo Merli shares the story behind the creation of BookKeeper, the va...
Jun 09, 2021•42 min•Ep. 193
Summary SQL is the most widely used language for working with data, and yet the tools available for writing and collaborating on it are still clunky and inefficient. Frustrated with the lack of a modern IDE and collaborative workflow for managing the SQL queries and analysis of their big data environments, the team at Pinterest created Querybook. In this episode Justin Mejorada-Pier and Charlie Gu share the story of how the initial prototype for a data catalog ended up as one of their most widel...
Jun 03, 2021•53 min•Ep. 192
Summary Every part of the business relies on data, yet only a small team has the context and expertise to build and maintain workflows and data pipelines to transform, clean, and integrate it. In order for the true value of your data to be realized without burning out your engineers you need a way for everyone to get access to the information they care about. To help make that a more tractable problem Blake Burch co-founded Shipyard. In this episode he explains the utility of a low code solution...
Jun 02, 2021•51 min•Ep. 191
Summary The data warehouse has become the focal point of the modern data platform. With increased usage of data across businesses, and a diversity of locations and environments where data needs to be managed, the warehouse engine needs to be fast and easy to manage. Yellowbrick is a data warehouse platform that was built from the ground up for speed, and can work across clouds and all the way to the edge. In this episode CTO Mark Cusack explains how the engine is architected, the benefits that s...
May 28, 2021•53 min•Ep. 190
Summary Machine learning models use vectors as the natural mechanism for representing their internal state. The problem is that in order for the models to integrate with external systems their internal state has to be translated into a lower dimension. To eliminate this impedance mismatch Edo Liberty founded Pinecone to build database that works natively with vectors. In this episode he explains how this technology will allow teams to accelerate the speed of innovation, how vectors make it possi...
May 25, 2021•47 min•Ep. 189
Summary Data governance is a phrase that means many different things to many different people. This is because it is actually a concept that encompasses the entire lifecycle of data, across all of the people in an organization who interact with it. Stijn Christiaens co-founded Collibra with the goal of addressing the wide variety of technological aspects that are necessary to realize such an important and expansive process. In this episode he shares his thoughts on the balance between human and ...
May 21, 2021•56 min•Ep. 188
Summary Data lineage is the common thread that ties together all of your data pipelines, workflows, and systems. In order to get a holistic understanding of your data quality, where errors are occurring, or how a report was constructed you need to track the lineage of the data from beginning to end. The complicating factor is that every framework, platform, and product has its own concepts of how to store, represent, and expose that information. In order to eliminate the wasted effort of buildin...
May 18, 2021•58 min•Ep. 187
Summary There is a lot of attention on the database market and cloud data warehouses. While they provide a measure of convenience, they also require you to sacrifice a certain amount of control over your data. If you want to build a warehouse that gives you both control and flexibility then you might consider building on top of the venerable PostgreSQL project. In this episode Thomas Richter and Joshua Drake share their advice on how to build a production ready data warehouse with Postgres. Anno...
May 14, 2021•1 hr 15 min•Ep. 186
Summary Building an API for real-time data is a challenging project. Making it robust, scalable, and fast is a full time job. The team at Tinybird wants to make it easy to turn a continuous stream of data into a production ready API or data product. In this episode CEO Jorge Sancha explains how they have architected their system to handle high data throughput and fast response times, and why they have invested heavily in Clickhouse as the core of their platform. This is a great conversation abou...
May 11, 2021•54 min•Ep. 185
Summary 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 while complicating others, how it simplifies the developme...
May 07, 2021•40 min•Ep. 184
Summary 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 Beauchemin, CEO of Preset, and Lior Gavish, CTO of Monte Carlo, ...
May 04, 2021•57 min•Ep. 183
Summary 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 data and deliver analytics. He digs into how it integrates...
Apr 27, 2021•47 min•Ep. 182
Summary 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 language processing and entity resolution system that is served...
Apr 20, 2021•48 min•Ep. 181
Summary "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 effectively. He also talks about how his experience working with these te...
Apr 13, 2021•1 hr 9 min•Ep. 180
Summary 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 shares her experiences across multiple attempts at building...
Apr 06, 2021•58 min•Ep. 179
