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Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Datalakes are notoriously complex. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data.
Summary A data lakehouse is intended to combine the benefits of datalakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Datalakes are notoriously complex. To start, can you share your definition of what constitutes a "Data Lakehouse"?
Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. Datalakes are notoriously complex. Materialize]([link] You shouldn't have to throw away the database to build with fast-changing data.
In this episode Tobias Macey shares his thoughts on the challenges that he is facing as he prepares to build the next set of architectural layers for his data platform to enable a larger audience to start accessing the data being managed by his team. Datalakes are notoriously complex. With Materialize, you can!
TL;DR After setting up and organizing the teams, we are describing 4 topics to make data mesh a reality. With this 3rd platform generation, you have more real time data analytics and a cost reduction because it is easier to manage this infrastructure in the cloud thanks to managed services.
Summary 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. Datalakes in various forms have been gaining significant popularity as a unified interface to an organization's analytics.
Datalakes are notoriously complex. For data engineers who battle to build and scale high quality dataworkflows on the datalake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
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. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Datalakes are notoriously complex.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is brought to you by Datafold – a testing automation platform for data engineers that prevents data quality issues from entering every part of your dataworkflow, from migration to dbt deployment.
It incorporates elements from several Microsoft products working together, like Power BI, Azure Synapse Analytics, Data Factory, and OneLake, into a single SaaS experience. Its multi-cluster shared dataarchitecture is one of its primary features.
Datalakes are notoriously complex. For data engineers who battle to build and scale high quality dataworkflows on the datalake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
Datalakes are notoriously complex. For data engineers who battle to build and scale high quality dataworkflows on the datalake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
Datalakes are notoriously complex. For data engineers who battle to build and scale high quality dataworkflows on the datalake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
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. Can you describe what role Trino and Iceberg play in Stripe's dataarchitecture?
Datalakes are notoriously complex. How much of the complexity is due to the nature of streaming data vs. the architectural realities of Flink? How has the lack of visibility into the flow of data in Flink impacted the ways that teams think about where/when/how to apply it? Datalakes are notoriously complex.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is brought to you by Datafold – a testing automation platform for data engineers that prevents data quality issues from entering every part of your dataworkflow, from migration to dbt deployment.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Datalakes are notoriously complex. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data.
Summary 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. How has that changed the architectural approach to CDPs? Want to see Starburst in action?
Datalakes are notoriously complex. For data engineers who battle to build and scale high quality dataworkflows on the datalake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
Summary Data lakehouse architectures are gaining popularity due to the flexibility and cost effectiveness that they offer. The link that bridges the gap between datalake and warehouse capabilities is the catalog. Datalakes are notoriously complex. Can you describe the architecture of Nessie?
Datalakes are notoriously complex. For data engineers who battle to build and scale high quality dataworkflows on the datalake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
Summary A significant portion of dataworkflows involve storing and processing information in database engines. 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. Datalakes are notoriously complex.
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. Want to see Starburst in action?
Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. Datafold has recently launched a 3-in-1 product experience to support accelerated data migrations. Datalakes are notoriously complex.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Datalakes are notoriously complex. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data.
Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. Datalakes are notoriously complex. Materialize]([link] You shouldn't have to throw away the database to build with fast-changing data.
Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. Datalakes are notoriously complex. Materialize]([link] You shouldn't have to throw away the database to build with fast-changing data.
If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold Datalakes are notoriously complex. With Materialize, you can! Sponsored By: Starburst : ![Starburst
In this episode Andrey Korchack, CTO of fintech startup Monite, discusses the complexities of designing and implementing a data platform in that sector. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Datalakes are notoriously complex.
Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. Datalakes are notoriously complex. Materialize]([link] You shouldn't have to throw away the database to build with fast-changing data.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management You shouldn't have to throw away the database to build with fast-changing data. Datalakes are notoriously complex. With Materialize, you can! That’s three free boards at dataengineeringpodcast.com/miro.
[link] Adam Bellemare & Thomas Betts: The End of the Bronze Age: Rethinking the Medallion Architecture I’m always a bit uncomfortable with medallion architecture since it is a glorified term for the traditional ETL process. link] All rights reserved ProtoGrowth Inc, India.
Data pipelines are the backbone of your business’s dataarchitecture. Implementing a robust and scalable pipeline ensures you can effectively manage, analyze, and organize your growing data. Benjamin Kennedy, Cloud Solutions Architect at Striim, emphasizes the outcome-driven nature of data pipelines.
DataOps Architecture: 5 Key Components and How to Get Started Ryan Yackel August 30, 2023 What Is DataOps Architecture? DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. As a result, they can be slow, inefficient, and prone to errors.
In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? Why is data pipeline architecture important?
RudderStack helps you build a customer data platform on your warehouse or datalake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control.
Focus areas include: Migrating your current data functionalities and KPIs to Snowflake. Determining an architecture and a scalable data model to integrate more source systems in the future. top modernizing your datalake with Snowflake, watch our on demand webinar.
In order to quickly identify if and how two data systems are out of sync Gleb Mezhanskiy and Simon Eskildsen partnered to create the open source data-diff utility. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability.
When an especially gifted data engineer decided to leave, it left the team scrambling to support routine functions. The dynamic nature of the consulting team meant that architectural decisions made at the data engineering level were often short-sighted and incoherent.
Evolution of DataLake Technologies The datalake ecosystem has matured significantly in 2024, particularly in table formats and storage technologies. This architecture incorporates real-time query processing and semantic search capabilities, enabling faster and more accurate content discovery.
As organizations seek greater value from their data, dataarchitectures are evolving to meet the demand — and table formats are no exception. It was designed to support high-volume data exchange and compatibility across different system versions, which is essential for streaming architectures such as Apache Kafka.
Integrating AI into dataworkflows is not just a trend but a paradigm shift, making data processes more efficient and intelligent. Lake House Architectures: The New Frontier Lakehouse architectures have been at the forefront of data engineering discussions this year.
There’s a recent trend toward people creating datalake or data warehouse patterns and calling it data enablement or a data hub. DataOps expands upon this approach by focusing on the processes and workflows that create data enablement and business analytics. DataOps Process Hub.
Moreover, it facilitates the implementation of microservices architectures and event-driven systems, automating reactions to data changes without manual intervention. It captures incremental changes from transactional databases or other sources, efficiently loading them into data warehouses or datalakes.
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