Build Confidence In Your Data Platform With Schema Compatibility Reports That Span Systems And Domains Using Schemata

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Improve Data Quality Through Engineering Rigor And Business Engagement With Synq: This episode features an insightful conversation with Petr Janda, the CEO and founder of Synq. Petr shares his journey from being an engineer to founding Synq, emphasizing the importance of treating data systems with the same rigor as engineering systems. He discusses the challenges and solutions in data reliability, including the need for transparency and ownership in data systems. Synq's platform helps data teams manage incidents, understand data dependencies, and ensure data quality by providing insights and automation capabilities. Petr emphasizes the need for a holistic approach to data reliability, integrating data systems into broader business processes. He highlights the role of data teams in modern organizations and how Synq is empowering them to achieve this. by Data Engineering Podcast

60 min listen

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Stitching Together Enterprise Analytics With Microsoft Fabric: Data lakehouse architectures have been gaining significant adoption. To accelerate adoption in the enterprise Microsoft has created the Fabric platform, based on their OneLake architecture. In this episode Dipti Borkar shares her experiences working on the product team at Fabric and explains the various use cases for the Fabric service. by Data Engineering Podcast

53 min listen

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Being Data Driven At Stripe With Trino And Iceberg: Stripe is a company that relies on data to power their products and business. To support that functionality they have invested in Trino and Iceberg for their analytical workloads. In this episode Kevin Liu shares some of the interesting features that they have built by combining those technologies, as well as the challenges that they face in supporting the myriad workloads that are thrown at this layer of their data platform. by Data Engineering Podcast

53 min listen

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X-Ray Vision For Your Flink Stream Processing With Datorios: Streaming data processing enables new categories of data products and analytics. Unfortunately, reasoning about stream processing engines is complex and lacks sufficient tooling. To address this shortcoming Datorios created an observability platform for Flink that brings visibility to the internals of this popular stream processing system. In this episode Ronen Korman and Stav Elkayam discuss how the increased understanding provided by purpose built observability improves the usefulness of Flink. by Data Engineering Podcast

42 min listen

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Practical First Steps In Data Governance For Long Term Success: Modern businesses aspire to be data driven, and technologists enjoy working through the challenge of building data systems to support that goal. Data governance is the binding force between these two parts of the organization. Nicola Askham found her way into data governance by accident, and stayed because of the benefit that she was able to provide by serving as a bridge between the technology and business. In this episode she shares the practical steps to implementing a data governance practice in your organization, and the pitfalls to avoid. by Data Engineering Podcast

61 min listen

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Data Migration Strategies For Large Scale Systems: Any software system that survives long enough will require some form of migration or evolution. When that system is responsible for the data layer the process becomes more challenging. Sriram Panyam has been involved in several projects that required migration of large volumes of data in high traffic environments. In this episode he shares some of the valuable lessons that he learned about how to make those projects successful. by Data Engineering Podcast

60 min listen

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Zenlytic Is Building You A Better Coworker With AI Agents: The purpose of business intelligence systems is to allow anyone in the business to access and decode data to help them make informed decisions. Unfortunately this often turns into an exercise in frustration for everyone involved due to complex workflows and hard-to-understand dashboards. The team at Zenlytic have leaned on the promise of large language models to build an AI agent that lets you converse with your data. In this episode they share their journey through the fast-moving landscape of generative AI and unpack the difference between an AI chatbot and an AI agent. by Data Engineering Podcast

54 min listen

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Release Management For Data Platform Services And Logic: Building a data platform is a substrantial engineering endeavor. Once it is running, the next challenge is figuring out how to address release management for all of the different component parts. The services and systems need to be kept up to date, but so does the code that controls their behavior. In this episode your host Tobias Macey reflects on his current challenges in this area and some of the factors that contribute to the complexity of the problem. by Data Engineering Podcast

20 min listen

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Barking Up The Wrong GPTree: Building Better AI With A Cognitive Approach: Artificial intelligence has dominated the headlines for several months due to the successes of large language models. This has prompted numerous debates about the possibility of, and timeline for, artificial general intelligence (AGI). Peter Voss has dedicated decades of his life to the pursuit of truly intelligent software through the approach of cognitive AI. In this episode he explains his approach to building AI in a more human-like fashion and the emphasis on learning rather than statistical prediction. by Data Engineering Podcast

54 min listen

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Build Your Second Brain One Piece At A Time: Generative AI promises to accelerate the productivity of human collaborators. Currently the primary way of working with these tools is through a conversational prompt, which is often cumbersome and unwieldy. In order to simplify the integration of AI capabilities into developer workflows Tsavo Knott helped create Pieces, a powerful collection of tools that complements the tools that developers already use. In this episode he explains the data collection and preparation process, the collection of model types and sizes that work together to power the experience, and how to incorporate it into your workflow to act as a second brain. by Data Engineering Podcast

50 min listen

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Making Email Better With AI At Shortwave: Generative AI has rapidly transformed everything in the technology sector. When Andrew Lee started work on Shortwave he was focused on making email more productive. When AI started gaining adoption he realized that he had even more potential for a transformative experience. In this episode he shares the technical challenges that he and his team have overcome in integrating AI into their product, as well as the benefits and features that it provides to their customers. by Data Engineering Podcast

54 min listen

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Designing A Non-Relational Database Engine: Databases come in a variety of formats for different use cases. The default association with the term "database" is relational engines, but non-relational engines are also used quite widely. In this episode Oren Eini, CEO and creator of RavenDB, explores the nuances of relational vs. non-relational engines, and the strategies for designing a non-relational database. by Data Engineering Podcast

76 min listen

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Establish A Single Source Of Truth For Your Data Consumers With A Semantic Layer: Maintaining a single source of truth for your data is the biggest challenge in data engineering. Different roles and tasks in the business need their own ways to access and analyze the data in the organization. In order to enable this use case, while maintaining a single point of access, the semantic layer has evolved as a technological solution to the problem. In this episode Artyom Keydunov, creator of Cube, discusses the evolution and applications of the semantic layer as a component of your data platform, and how Cube provides speed and cost optimization for your data consumers. by Data Engineering Podcast

56 min listen

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Adding Anomaly Detection And Observability To Your dbt Projects Is Elementary: Working with data is a complicated process, with numerous chances for something to go wrong. Identifying and accounting for those errors is a critical piece of building trust in the organization that your data is accurate and up to date. While there are numerous products available to provide that visibility, they all have different technologies and workflows that they focus on. To bring observability to dbt projects the team at Elementary embedded themselves into the workflow. In this episode Maayan Salom explores the approach that she has taken to bring observability, enhanced testing capabilities, and anomaly detection into every step of the dbt developer experience. by Data Engineering Podcast

51 min listen

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Ship Smarter Not Harder With Declarative And Collaborative Data Orchestration On Dagster+: A core differentiator of Dagster in the ecosystem of data orchestration is their focus on software defined assets as a means of building declarative workflows. With their launch of Dagster+ as the redesigned commercial companion to the open source project they are investing in that capability with a suite of new features. In this episode Pete Hunt, CEO of Dagster labs, outlines these new capabilities, how they reduce the burden on data teams, and the increased collaboration that they enable across teams and business units. by Data Engineering Podcast

56 min listen

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Reconciling The Data In Your Databases With Datafold: A significant portion of data workflows involve storing and processing information in database engines. Validating that the information is stored and processed correctly can be complex and time-consuming, especially when the source and destination speak different dialects of SQL. In this episode Gleb Mezhanskiy, founder and CEO of Datafold, discusses the different error conditions and solutions that you need to know about to ensure the accuracy of your data. by Data Engineering Podcast

58 min listen

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Version Your Data Lakehouse Like Your Software With Nessie: Data lakehouse architectures are gaining popularity due to the flexibility and cost effectiveness that they offer. The link that bridges the gap between data lake and warehouse capabilities is the catalog. The primary purpose of the catalog is to inform the query engine of what data exists and where, but the Nessie project aims to go beyond that simple utility. In this episode Alex Merced explains how the branching and merging functionality in Nessie allows you to use the same versioning semantics for your data lakehouse that you are used to from Git. by Data Engineering Podcast

41 min listen

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When And How To Conduct An AI Program: Artificial intelligence technologies promise to revolutionize business and produce new sources of value. In order to make those promises a reality there is a substantial amount of strategy and investment required. Colleen Tartow has worked across all stages of the data lifecycle, and in this episode she shares her hard-earned wisdom about how to conduct an AI program for your organization. by Data Engineering Podcast

46 min listen

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Find Out About The Technology Behind The Latest PFAD In Analytical Database Development: Building a database engine requires a substantial amount of engineering effort and time investment. Over the decades of research and development into building these software systems there are a number of common components that are shared across implementations. When Paul Dix decided to re-write the InfluxDB engine he found the Apache Arrow ecosystem ready and waiting with useful building blocks to accelerate the process. In this episode he explains how he used the combination of Apache Arrow, Flight, Datafusion, and Parquet to lay the foundation of the newest version of his time-series database. by Data Engineering Podcast

56 min listen

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Using Trino And Iceberg As The Foundation Of Your Data Lakehouse: A data lakehouse is intended to combine the benefits of data lakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Multiple open source projects and vendors have been working together to make this vision a reality. In this episode Dain Sundstrom, CTO of Starburst, explains how the combination of the Trino query engine and the Iceberg table format offer the ease of use and execution speed of data warehouses with the infinite storage and scalability of data lakes. by Data Engineering Podcast

59 min listen

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Data Sharing Across Business And Platform Boundaries: Sharing data is a simple concept, but complicated to implement well. There are numerous business rules and regulatory concerns that need to be applied. There are also numerous technical considerations to be made, particularly if the producer and consumer of the data aren't using the same platforms. In this episode Andrew Jefferson explains the complexities of building a robust system for data sharing, the techno-social considerations, and how the Bobsled platform that he is building aims to simplify the process. by Data Engineering Podcast

60 min listen

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Tackling Real Time Streaming Data With SQL Using RisingWave: Stream processing systems have long been built with a code-first design, adding SQL as a layer on top of the existing framework. RisingWave is a database engine that was created specifically for stream processing, with S3 as the storage layer. In this episode Yingjun Wu explains how it is architected to power analytical workflows on continuous data flows, and the challenges of making it responsive and scalable. by Data Engineering Podcast

57 min listen

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Build A Data Lake For Your Security Logs With Scanner: Monitoring and auditing IT systems for security events requires the ability to quickly analyze massive volumes of unstructured log data. The majority of products that are available either require too much effort to structure the logs, or aren't fast enough for interactive use cases. Cliff Crosland co-founded Scanner to provide fast querying of high scale log data for security auditing. In this episode he shares the story of how it got started, how it works, and how you can get started with it. by Data Engineering Podcast

63 min listen

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Modern Customer Data Platform Principles: Databases and analytics architectures have gone through several generational shifts. A substantial amount of the data that is being managed in these systems is related to customers and their interactions with an organization. In this episode Tasso Argyros, CEO of ActionIQ, gives a summary of the major epochs in database technologies and how he is applying the capabilities of cloud data warehouses to the challenge of building more comprehensive experiences for end-users through a modern customer data platform (CDP). by Data Engineering Podcast

62 min listen

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Pushing The Limits Of Scalability And User Experience For Data Processing WIth Jignesh Patel: Data processing technologies have dramatically improved in their sophistication and raw throughput. Unfortunately, the volumes of data that are being generated continue to double, requiring further advancements in the platform capabilities to keep up. As the sophistication increases, so does the complexity, leading to challenges for user experience. Jignesh Patel has been researching these areas for several years in his work as a professor at Carnegie Mellon University. In this episode he illuminates the landscape of problems that we are faced with and how his research is aimed at helping to solve these problems. by Data Engineering Podcast

50 min listen

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Designing Data Platforms For Fintech Companies: Working with financial data requires a high degree of rigor due to the numerous regulations and the risks involved in security breaches. In this episode Andrey Korchack, CTO of fintech startup Monite, discusses the complexities of designing and implementing a data platform in that sector. by Data Engineering Podcast

48 min listen

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Troubleshooting Kafka In Production: Kafka has become a ubiquitous technology, offering a simple method for coordinating events and data across different systems. Operating it at scale, however, is notoriously challenging. Elad Eldor has experienced these challenges first-hand, leading to his work writing the book "Kafka: Troubleshooting in Production". In this episode he highlights the sources of complexity that contribute to Kafka's operational difficulties, and some of the main ways to identify and mitigate potential sources of trouble. by Data Engineering Podcast

75 min listen

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Adding An Easy Mode For The Modern Data Stack With 5X: The "modern data stack" promised a scalable, composable data platform that gave everyone the flexibility to use the best tools for every job. The reality was that it left data teams in the position of spending all of their engineering effort on integrating systems that weren't designed with compatible user experiences. The team at 5X understand the pain involved and the barriers to productivity and set out to solve it by pre-integrating the best tools from each layer of the stack. In this episode founder Tarush Aggarwal explains how the realities of the modern data stack are impacting data teams and the work that they are doing to accelerate time to value. by Data Engineering Podcast

56 min listen

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Run Your Own Anomaly Detection For Your Critical Business Metrics With Anomstack: If your business metrics looked weird tomorrow, would you know about it first? Anomaly detection is focused on identifying those outliers for you, so that you are the first to know when a business critical dashboard isn't right. Unfortunately, it can often be complex or expensive to incorporate anomaly detection into your data platform. Andrew Maguire got tired of solving that problem for each of the different roles he has ended up in, so he created the open source Anomstack project. In this episode he shares what it is, how it works, and how you can start using it today to get notified when the critical metrics in your business aren't quite right. by Data Engineering Podcast

51 min listen

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Designing Data Transfer Systems That Scale: The first step of data pipelines is to move the data to a place where you can process and prepare it for its eventual purpose. Data transfer systems are a critical component of data enablement, and building them to support large volumes of information is a complex endeavor. Andrei Tserakhau has dedicated his careeer to this problem, and in this episode he shares the lessons that he has learned and the work he is doing on his most recent data transfer system at DoubleCloud. by Data Engineering Podcast

64 min listen

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Build Confidence In Your Data Platform With Schema Compatibility Reports That Span Systems And Domains Using Schemata

Build Confidence In Your Data Platform With Schema Compatibility Reports That Span Systems And Domains Using Schemata

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