๐ ๐๐ฑ๐๐ข๐ญ๐ข๐ง๐ ๐๐ข๐ฆ๐๐ฌ ๐ข๐ง ๐ญ๐ก๐ ๐๐๐ญ๐ ๐๐๐ค๐๐ก๐จ๐ฎ๐ฌ๐ ๐๐จ๐ซ๐ฅ๐! ๐ ๐ Its already Tuesday, and a suspicious absence looms: no new catalog has surfaced this week. But fear notโ The ๐ง Iceberg Catalog landscape is evolving rapidly with significant announcements from Snowflake and Databricks. At HANSETAG, we're thrilled to introduce ๐๐๐ - our ๐ฆ #Rust-native #OSS #Iceberg #REST Catalog designed for superior data quality, governance, and flexibility. ๐ ๐๐๐ ๐ฉ๐ซ๐จ๐ฏ๐ข๐๐๐ฌ: โข ๐๐ฎ๐ฅ๐ญ๐ข-๐ญ๐๐ง๐๐ง๐y: Manage multiple projects and warehouses effortlessly. โข ๐๐ก๐๐ง๐ ๐ ๐๐ฏ๐๐ง๐ญ๐ฌ: Stay updated with every table change. โข ๐๐จ๐ง๐ญ๐ซ๐๐๐ญ ๐๐๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง: Ensure seamless data operations. โข ๐๐ฎ๐ฌ๐ญ๐จ๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง: Extend TIP with ease using our built-in and public interfaces. Explore more: https://lnkd.in/ev5N_WgG #DataLakehouse #DataGovernance #OpenSource #Rust #IcebergCatalog #DataContract #DataMesh #DataProduct #Iceberg #DataProduct
Dr. Tim Dรถrnemannโs Post
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๐ ๐๐ฑ๐๐ข๐ญ๐ข๐ง๐ ๐๐ข๐ฆ๐๐ฌ ๐ข๐ง ๐ญ๐ก๐ ๐๐๐ญ๐ ๐๐๐ค๐๐ก๐จ๐ฎ๐ฌ๐ ๐๐จ๐ซ๐ฅ๐! ๐ ๐ Its already Tuesday, and a suspicious absence looms: no new catalog has surfaced this week. But fear notโ The ๐ง Iceberg Catalog landscape is evolving rapidly with significant announcements from Snowflake and Databricks. At HANSETAG, we're thrilled to introduce ๐๐๐ - our ๐ฆ #Rust-native #OSS #Iceberg #REST Catalog designed for superior data quality, governance, and flexibility. ๐ ๐๐๐ ๐ฉ๐ซ๐จ๐ฏ๐ข๐๐๐ฌ: โข ๐๐ฎ๐ฅ๐ญ๐ข-๐ญ๐๐ง๐๐ง๐y: Manage multiple projects and warehouses effortlessly. โข ๐๐ก๐๐ง๐ ๐ ๐๐ฏ๐๐ง๐ญ๐ฌ: Stay updated with every table change. โข ๐๐จ๐ง๐ญ๐ซ๐๐๐ญ ๐๐๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง: Ensure seamless data operations. โข ๐๐ฎ๐ฌ๐ญ๐จ๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง: Extend TIP with ease using our built-in and public interfaces. Explore more: https://lnkd.in/ev5N_WgG #DataLakehouse #DataGovernance #OpenSource #Rust #IcebergCatalog #DataContract #DataMesh #DataProduct #Iceberg #DataProduct
Iceberg Catalog: The TIP of your Lakehouse
medium.com
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There is a lot of dynamics in the #Lakehouse stack these days. It is high time that the community comes to an agreement for a common open catalog standard that can replace the increasingly dated #hivemetastore. For instance, Databricks (#DeltaLake ecosystem) open sourced #UnityCatalog last week: https://lnkd.in/d48Qvm44. Now, this catalog here by HANSETAG is an open source implementation of the #Iceberg catalog REST API, which comes with many bells and whistles for #enterprise deployments such as secure credential mapping. These two are not necessarily just disjunct alternatives when you take into account #Databricks acquisition of Tabular (now part of Databricks) and the plans they shared last week for convergence of #DeltaLake and #Iceberg: https://lnkd.in/dSC2zEf7 At the end of the day people will want a catalog that supports all established table formats and comes with the critical enterprise bells and whistles!
๐ ๐๐ฑ๐๐ข๐ญ๐ข๐ง๐ ๐๐ข๐ฆ๐๐ฌ ๐ข๐ง ๐ญ๐ก๐ ๐๐๐ญ๐ ๐๐๐ค๐๐ก๐จ๐ฎ๐ฌ๐ ๐๐จ๐ซ๐ฅ๐! ๐ ๐ Its already Tuesday, and a suspicious absence looms: no new catalog has surfaced this week. But fear notโ The ๐ง Iceberg Catalog landscape is evolving rapidly with significant announcements from Snowflake and Databricks. At HANSETAG, we're thrilled to introduce ๐๐๐ - our ๐ฆ #Rust-native #OSS #Iceberg #REST Catalog designed for superior data quality, governance, and flexibility. ๐ ๐๐๐ ๐ฉ๐ซ๐จ๐ฏ๐ข๐๐๐ฌ: โข ๐๐ฎ๐ฅ๐ญ๐ข-๐ญ๐๐ง๐๐ง๐y: Manage multiple projects and warehouses effortlessly. โข ๐๐ก๐๐ง๐ ๐ ๐๐ฏ๐๐ง๐ญ๐ฌ: Stay updated with every table change. โข ๐๐จ๐ง๐ญ๐ซ๐๐๐ญ ๐๐๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง: Ensure seamless data operations. โข ๐๐ฎ๐ฌ๐ญ๐จ๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง: Extend TIP with ease using our built-in and public interfaces. Explore more: https://lnkd.in/ev5N_WgG #DataLakehouse #DataGovernance #OpenSource #Rust #IcebergCatalog #DataContract #DataMesh #DataProduct #Iceberg #DataProduct
Iceberg Catalog: The TIP of your Lakehouse
medium.com
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For those you have doubts about Data Catalogs, the following article can help clarify them. With the current rise in the news about Snowflake's Polaris Catalog and Databricks' Unity Catalog, it is good to understand what these catalogs are and what are the types of catalogs available. There are three types of data catalogs: - Technical catalogs (which focus on metadata for individual tools). - Embedded catalogs (which integrate metadata within specific data products). - Universal catalogs (which centralize metadata across an entire data estate). Each type serves different purposes in managing and understanding complex data environments, but are they fit for the present and future of the business needs? The traditional catalogs, whether technical, embedded, or universal, may not fully meet the diverse needs of modern data ecosystems. As data tools and users become increasingly varied, and as metadata itself evolves, there's a need for a more advanced solutionโone that goes beyond simple cataloging. What will be the natural evolution of data catalogs? #Datacatalog #Dataliteracy #Datagovernance
The War of the Catalogs
metadataweekly.substack.com
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๐ Exciting News! ๐ ๐ Databricks has acquired #Tabular! Explore the implications of this acquisition on the data and analytics landscape. ๐ Read More: https://lnkd.in/gjW8MBE3 #databricks #iceberg #datalakehouse #DQLabs #deltalake #dataquality #dataobservability
Why did Databricks Acquire Tabular
https://www.dqlabs.ai
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News: #snowflakeSummit. Open Data Revolution Continues My Analysis: Snowflakeโs strategy around Iceberg is a nuanced topic with significant implications. While the simplicity of Snowflakeโs compute services is enhanced by Iceberg, the real value lies in how Snowflake chooses to handle interoperability and external engine support. The Polaris news might crucial in defining its position in the ever-evolving data platform market. Polaris is another #opensource gift from Netflix. Implications: Many including us were asking..will Snowflake choose to embrace Iceberg and its open data potential, even if it means disrupting Snowflakeโs current model? Looks like the team is going to disrupt themselves to bring in a new era in open data. The technology industry has a saying, especially true during major market shifts: โBetter to eat your own before someone else does.โ This means that companies should cannibalize their own advantages before competitors do. For Snowflake, this is happening as the fully embracing Iceberg and its open data capabilities before competitors leverage these advantages against them.
Snowflake catalog supports cross-engine access to Iceberg data - SiliconANGLE
siliconangle.com
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"Starburst not only broadens its product offerings but also positions itself as a strong rival to established proprietary data warehouse vendors such as Teradata and Snowflake by integrating real-time data capabilities and supporting Snowflake Iceberg Catalogs." Check out this The Futurum Group article by Paul Nashawaty and Sam Holschuh about Starburst's latest Icehouse announcements: https://okt.to/yh8XjL #Icehouse #trinodb #ApacheIceberg
Starburst Ignites Data Innovation with Enhanced Icehouse Architecture
futurumgroup.com
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Top 10 Industry Analysts ranked by ARchitect Analyst Power 100 | Practice Leader | Application Development | Open Source | Business Strategy
Sam H. and I had a great briefing today with Starburst. Vishal Singh and Manveer Sahota provided an update on the company, its partnerships with Dell Technologies and Google, and a great in-depth demo of #StarbustGalaxy, their SaaS offering showcasing an end-to-end analytics platform for both cloud and on-premises workloads. Another area I have to highlight is open-source projects. Trino is a distributed SQL query engine built in Java and is open source. It's very cool to check out. It is an efficient tool for running analytic queries against various data sources, ranging in size from gigabytes to petabytes. Trino was created to provide a platform for interactive data analytics that can deliver the same speed as commercial data warehouses while scaling up to the size of the largest organizations in the world. Trino was initially developed at Facebook under the name Presto. It was designed to handle the scale and performance requirements for querying their Hive-based data warehouse. However, Trino has been expanded to connect to many other data sources. Trinoโs architecture is designed to combine the best of both traditional shared-nothing and shared-disk systems. It is built on a distributed cluster model where each node in the cluster has compute and storage resources. This allows Trino to scale horizontally across multiple nodes, with each node capable of handling a portion of the workload. This architecture ensures that Trino can handle massive amounts of data with ease. Trino is optimized for interactive queries and supports a wide range of data formats and data sources. It can be used with popular databases like MySQL, PostgreSQL, and MongoDB, as well as cloud storage solutions like Amazon S3 and Microsoft Azure. Additionally, Trino supports a range of data formats, including CSV, JSON, Avro, and Parquet. Definitely worth checking out! The Futurum Group #opensource Meta Amazon Web Services (AWS) Microsoft #postgresql MongoDB Justin Borgman #multicloud #distributedcloud
Former sysadmin and storage consultant, present cat herder for Tech Field Day, future old man shouting โon-premisesโ at clouds. I talk to cameras a lot.
Starburst CEO Justin Borgman recently outlined a vision for the future of data architecture with "The Icehouse Manifesto: Building an Open Lakehouse." I've written and been briefed by Starburst before, and am convinced that this approach could fundamentally change the data management landscape. The manifesto introduces a lakehouse architecture that leverages #Trino for querying and #Iceberg for table formatting, and Justin advocates for an open and flexible system that simplifies data operations and enhances scalability by using standard #SQL across a decentralized platform. For organizations looking to evolve their data strategy and embrace a more open, agile approach to data warehousing, his concept ought to make a lot of sense. Consider the potential of an open lakehouse as detailed in the manifesto, which could be the key to unlocking greater innovation and efficiency in your data ecosystem. Sam Holschuh Beth Winkowski Jay Chen Tobias Ternstrรถm The Futurum Group
The Icehouse Manifesto: Building an Open Lakehouse
https://www.starburst.io
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Former sysadmin and storage consultant, present cat herder for Tech Field Day, future old man shouting โon-premisesโ at clouds. I talk to cameras a lot.
Starburst CEO Justin Borgman recently outlined a vision for the future of data architecture with "The Icehouse Manifesto: Building an Open Lakehouse." I've written and been briefed by Starburst before, and am convinced that this approach could fundamentally change the data management landscape. The manifesto introduces a lakehouse architecture that leverages #Trino for querying and #Iceberg for table formatting, and Justin advocates for an open and flexible system that simplifies data operations and enhances scalability by using standard #SQL across a decentralized platform. For organizations looking to evolve their data strategy and embrace a more open, agile approach to data warehousing, his concept ought to make a lot of sense. Consider the potential of an open lakehouse as detailed in the manifesto, which could be the key to unlocking greater innovation and efficiency in your data ecosystem. Sam Holschuh Beth Winkowski Jay Chen Tobias Ternstrรถm The Futurum Group
The Icehouse Manifesto: Building an Open Lakehouse
https://www.starburst.io
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With #SnowflakeSummit around the corner, we're on a mission to help you digest all that's possible in Snowflakeโshining a light on the areas that help your organization get the most value out of your data. Our #SnowflakePanorama โ๏ธ series dives deep into functionalityโultimately giving you a big picture understanding of all the ways you can access and enrich data for innovation and competitive advantage. Part one of the series, written by Kwon K. Lee, focuses on Data Engineering.
Data Engineering: The Backbone of Snowflake Data Cloud
https://www.onesixsolutions.com
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