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In this episode Ori Rafael shares his experiences from Upsolver and building scalable stream processing for integrating and analyzing data, and what the tradeoffs are when coming from a batch oriented mindset. Can you start by giving an overview of the state of the market for data lakes today?
Preamble Hello and welcome to the Data Engineering Podcast, the show about modern datamanagement When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagement When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. What are your goals with this book?
Pulsar is a well engineered and robust platform for building the core of any system that relies on durable access to easily scalable streams of data. What is Pulsar’s role in the lifecycle of data and where does it fit in the overall ecosystem of data tools? Can you start by giving an overview of what Pulsar is?
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. Can you describe what is driving the adoption of real-time analytics?
Confluent Tableflow can bridge Kafka and Iceberg data, but that is just a data movement that data integration tools like Fivetran or Airbyte can also achieve. On the other hand, Fluss is a Kappa Architecture ; it stores one copy of data and presents it as a stream or a table, depending on the use case.
What used to be entirely managed by the database engine is now a composition of multiple systems that need to be properly configured to work in concert. In order to bring the DBA into the new era of datamanagement the team at Upsolver added a SQL interface to their data lake platform. We talked last in November of 2018.
This conversation was useful for getting a better idea of the challenges that exist in large scale data analytics, and the current state of the tradeoffs between data lakes and data warehouses in the cloud. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference.
This article aims to shed light on the essential components of successful data pipeline architectures and offer actionable advice for creating scalable, reliable, and resilient data pipelines. Ready to fortify your datamanagement practice? Let’s dive into the world of data pipeline architecture.
Data ingestion is the process of collecting data from various sources and moving it to your data warehouse or lake for processing and analysis. It is the first step in modern datamanagement workflows. Some data teams will leverage micro-batch strategies for time sensitive use cases.
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