Data Engineering Podcast

Summary Dan Delorey helped to build the core technologies of Google’s cloud data services for many years before embarking on his latest adventure as the VP of Data at SoFi. From being an early engineer on the Dremel project, to helping launch and manage BigQuery, on to helping enterprises adopt Google’s data products he learned all of the critical details of how to run services used by...

Summary Many of the events, ideas, and objects that we try to represent through data have a high degree of connectivity in the real world. These connections are best represented and analyzed as graphs to provide efficient and accurate analysis of their relationships. TigerGraph is a leading database that offers a highly scalable and performant native graph engine for powering graph analytics and m...

Summary The predominant pattern for data integration in the cloud has become extract, load, and then transform or ELT. Matillion was an early innovator of that approach and in this episode CTO Ed Thompson explains how they have evolved the platform to keep pace with the rapidly changing ecosystem. He describes how the platform is architected, the challenges related to selling cloud technologies in...

Summary Building a data platform is an iterative and evolutionary process that requires collaboration with internal stakeholders to ensure that their needs are being met. Yotpo has been on a journey to evolve and scale their data platform to continue serving the needs of their organization as it increases the scale and sophistication of data usage. In this episode Doron Porat and Liran Yogev expla...

Summary A huge amount of effort goes into modeling and shaping data to make it available for analytical purposes. This is often due to the need to simplify the final queries so that they are performant for visualization or limited exploration. In order to cut down the level of effort involved in making data usable, Matthew Halliday and his co-founders created Incorta as an end-to-end, in-memory an...

Summary There are very few tools which are equally useful for data engineers, data scientists, and machine learning engineers. WhyLogs is a powerful library for flexibly instrumenting all of your data systems to understand the entire lifecycle of your data from source to productionized model. In this episode Andy Dang explains why the project was created, how you can apply it to your existing data...

Summary The next paradigm shift in computing is coming in the form of quantum technologies. Quantum procesors have gained significant attention for their speed and computational power. The next frontier is in quantum networking for highly secure communications and the ability to distribute across quantum processing units without costly translation between quantum and classical systems. In this epi...

Summary Putting machine learning models into production and keeping them there requires investing in well-managed systems to manage the full lifecycle of data cleaning, training, deployment and monitoring. This requires a repeatable and evolvable set of processes to keep it functional. The term MLOps has been coined to encapsulate all of these principles and the broader data community is working t...

Summary Data engineering is a practice that is multi-faceted and requires integration with a large number of systems. This often means working across multiple tools to get the job done which can introduce significant cost to productivity due to the number of context switches. Rivery is a platform designed to reduce this incidental complexity and provide a single system for working across the diffe...

Summary Any time that you are storing data about people there are a number of privacy and security considerations that come with it. Privacy engineering is a growing field in data management that focuses on how to protect attributes of personal data so that the containing datasets can be shared safely. In this episode Gretel co-founder and CTO John Myers explains how they are building tools for da...

Summary The flexibility of software oriented data workflows is useful for fulfilling complex requirements, but for simple and repetitious use cases it adds significant complexity. Coalesce is a platform designed to reduce repetitive work for common workflows by adopting a visual pipeline builder to support your data warehouse transformations. In this episode Satish Jayanthi explains how he is buil...

Summary Building a data platform for your organization is a challenging undertaking. Building multiple data platforms for other organizations as a service without burning out is another thing entirely. In this episode Brandon Beidel from Red Ventures shares his experiences as a data product manager in charge of helping his customers build scalable analytics systems that fit their needs. He explain...

Summary At the foundational layer many databases and data processing engines rely on key/value storage for managing the layout of information on the disk. RocksDB is one of the most popular choices for this component and has been incorporated into popular systems such as ksqlDB. As these systems are scaled to larger volumes of data and higher throughputs the RocksDB engine can become a bottleneck...

Summary Data governance is a practice that requires a high degree of flexibility and collaboration at the organizational and technical levels. The growing prominence of cloud and hybrid environments in data management adds additional stress to an already complex endeavor. Privacera is an enterprise grade solution for cloud and hybrid data governance built on top of the robust and battle tested Apa...

Summary Data assets and the pipelines that create them have become critical production infrastructure for companies. This adds a requirement for reliability and management of up-time similar to application infrastructure. In this episode Francisco Alberini and Mei Tao share their insights on what incident management looks like for data platforms and the teams that support them. Announcements Hello...

Summary Data and analytics are permeating every system, including customer-facing applications. The introduction of embedded analytics to an end-user product creates a significant shift in requirements for your data layer. The Pinot OLAP datastore was created for this purpose, optimizing for low latency queries on rapidly updating datasets with highly concurrent queries. In this episode Kishore Go...

Summary Data observability is a term that has been co-opted by numerous vendors with varying ideas of what it should mean. At Acceldata, they view it as a holistic approach to understanding the computational and logical elements that power your analytical capabilities. In this episode Tristan Spaulding, head of product at Acceldata, explains the multi-dimensional nature of gaining visibility into...

Summary The modern data stack is a constantly moving target which makes it difficult to adopt without prior experience. In order to accelerate the time to deliver useful insights at organizations of all sizes that are looking to take advantage of these new and evolving architectures Tarush Aggarwal founded 5X Data. In this episode he explains how he works with these companies to deploy the technol...

Summary When you think about selecting a database engine for your project you typically consider options focused on serving multiple concurrent users. Sometimes what you really need is an embedded database that is blazing fast for single user workloads. DuckDB is an in-process database engine optimized for OLAP applications to speed up your analytical queries that meets you where you are, whether...

Summary Databases are an important component of application architectures, but they are often difficult to work with. HarperDB was created with the core goal of being a developer friendly database engine. In the process they ended up creating a scalable distributed engine that works across edge and datacenter environments to support a variety of novel use cases. In this episode co-founder and CEO...

Summary There are a wealth of options for managing structured and textual data, but unstructured binary data assets are not as well supported across the ecosystem. As organizations start to adopt cloud technologies they need a way to manage the distribution, discovery, and collaboration of data across their operating environments. To help solve this complicated challenge Krishna Subramanian and he...

Summary Building a data platform is a complex journey that requires a significant amount of planning to do well. It requires knowledge of the available technologies, the requirements of the operating environment, and the expectations of the stakeholders. In this episode Tobias Macey, the host of the show, reflects on his plans for building a data platform and what he has learned from running the p...

Summary The life sciences as an industry has seen incredible growth in scale and sophistication, along with the advances in data technology that make it possible to analyze massive amounts of genomic information. In this episode Guy Yachdav, director of software engineering for ImmunAI, shares the complexities that are inherent to managing data workflows for bioinformatics. He also explains how he...

Summary Python has grown to be one of the top languages used for all aspects of data, from collection and cleaning, to analysis and machine learning. Along with that growth has come an explosion of tools and engines that help power these workflows, which introduces a great deal of complexity when scaling from single machines and exploratory development to massively parallel distributed computation...

Summary Streaming data sources are becoming more widely available as tools to handle their storage and distribution mature. However it is still a challenge to analyze this data as it arrives, while supporting integration with static data in a unified syntax. Deephaven is a project that was designed from the ground up to offer an intuitive way for you to bring your code to your data, whether it is...