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Data Innovators Exchange
Public • 327 • Free
5 contributions to Data Innovators Exchange
17d ago in
Seeking Data Governance Essentials: Key Concepts & Must-Read Resources
Hi everyone - I’m looking to deepen my understanding of Data Governance and would love to hear about key concepts or frameworks you find essential. What are the must-know principles in this field? Also, if you have any “must-read” books or resources on Data Governance that you’d recommend, I’d appreciate your suggestions! Thanks in advance for sharing your insights! 😊
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New comment 1d ago
0 likes • 1d
I went with DAMA almost decade ago and I do not regret ever since then. Off course, every day professionals add content daily though the WWW, but without first reading the DAMA book, I would not "understand" them.
1d ago in
CDC or Brute Force Comparison
Which one you use for Data Ingestion: The Challenge of Consistency? Both have advantages. I found out, I need Brute Force Comparison in any case, but I implement CDC only for "near-real-time" ingestions and only on DB engines (like MS SQL), which have robust CDC capabilities already build-in and I can turn them on with a "click-of-a-button".
Poll
1 member has voted
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Aug 30 in
Snowflake vs. Databricks
Just finished watching a breakdown of Snowflake and Databricks. While the comparison itself isn't new, the focus on their origins and how that shaped their current offerings was insightful. Key takeaways: - Foundational Philosophies: Snowflake's roots in traditional data warehousing vs. Databricks' academic, notebook-centric beginnings are evident in their core strengths even today. - Architectural Choices: Snowflake's virtual warehouses & micro-partitions vs. Databricks' Spark clusters & Delta Lake highlight differing approaches to storage, compute & scalability. - The paths of the two platforms, Snowpark and the data lakehouse concept, reflect their initial goals. Share your thoughts! Which platform's philosophy resonates more with your data workflows?
Poll
7 members have voted
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New comment 7d ago
2 likes • 8d
I analyzed both Snowflake and Databricks conceps and I decided for the Databricks since it is more "open" - it can have simple "exist stategy". And after using Databricks for a while, I found out, it is even more "open" than I originally thought. It is easy to "feed" the data in and it is also easy to consume DBX data by any other "open" platforms (in parallel to Databricks!). Allow me to put this into perspective: how many Databases/Platforms allow you to feed/consume the data on a "storage" level with another Database/Platform? Does Oracle allows Microsoft to read Oracle DB files on the disk or even create DB files independently from any Oracle knowledge? Current support for Parquet files (which has Open Source standard), Delta lake, Iceberg makes Databricks clear winner on a long run.
Aug 30 in
What would you do if Data Vault would be forbidden?
Imagine a dystopian world, where you are not allowed to use Data Vault. How would you solve all the problems in your Data Warehouse?
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New comment 8d ago
0 likes • 8d
I would still implement it under allowed "name".
8d ago in
7-stage ingestion process
Data ingestion is more than just copying the data from Source systems into Analytical one. Here is Seven-stage strategy | LinkedIn how I handle it. Which stage you are missing or which one would you add to the list?
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Active 18h ago
Joined Nov 15, 2024
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