So you want to base your data lakehouse around Apache Iceberg to take advantage of its features, performance and vast ecosystem of platform/tool compatibility. You’ll need to take your current Hive tables and convert them to Iceberg. Iceberg offers several tools to do so depending on your needs and in this video we’ll explore that migration process.

Listen to Dremio's developer advocacy and engineering teams for an installment of Apache Iceberg Office Hours. During this time we’ll have a brief Iceberg presentation on Hidden Partitioning and Partitioning transforms in Iceberg and then lots of dedicated time for Q&A on the presented topic or any other questions or guidance you’re looking for help on in learning about Apache Iceberg or architecting your data lakehouse around Apache Iceberg.

Querying 100s of petabytes of data demands optimized query speed specifically when data accumulates over time. We have to ensure that the queries remain efficient because over time you may end up with a lot of small files and your data might not be optimally organized.

#datalakehouse #analytics #datawarehouse #datalake #opendatalakehouse #gnarlydatawaves #apacheiceberg #dremio #dremioartic #datamesh #metadata #modernization #datasharing #datagovernance #ETL #datasilos #datagrowth #selfservice #compliance #arctic #dataascode #branches #tags #optimized #automates #datamovement #zorder #clustering #metrics #filtering #partitioning #sorting #tableformat

The data lakehouse is quickly emerging as the ideal data architecture because it combines the flexibility and scalability of data lakes with the data management, data governance, and data analytics capabilities of data warehouses. Table formats bring many of the “house” features to the data lakehouse. Apache Iceberg is a truly open table format that is built for easy management and high performance analytics on the largest data volumes in the world.

As data lakes become the primary destination for growing volumes of customer and operational data, data teams need tools and processes that ensure data quality and consistency across data consumers and use cases. Join Dremio’s Jeremiah Morrow and Alex Merced as they discuss the emergence of data as code for data management, its benefits for data teams, and how Dremio customers are using it to deliver access to a consistent and accurate view of data in their data lakes. 

In this video on Gnarly Data Waves - Managing your data as code with Dremio Arctic, you will learn about:

- Why data as code is necessary for ensuring consistency and data quality for large data lakes.

- How Dremio Arctic uses Git-like concepts such as branches, tags, and commits to make data management easy.

- Some high value use cases for data as code.

See all upcoming episodes: https://www.dremio.com/gnarly-data-waves/?utm_medium=social-free&utm_source=youtube&utm_term=GDWEP8&utm_content=gdw-OD&utm_campaign=gdw-EP8

Connect with us!

Twitter: https://bit.ly/30pcpE1

LinkedIn: https://bit.ly/2PoqsDq

Facebook: https://bit.ly/2BV881V

Community Forum: https://bit.ly/2ELXT0W

Github: https://bit.ly/3go4dcM

Blog: https://bit.ly/2DgyR9B

Questions?: https://bit.ly/30oi8tX 

Website: https://bit.ly/2XmtEnN

#datalakehouse #analytics #datawarehouse #datalake #opendatalakehouse #gnarlydatawaves #apacheiceberg #dremio #dremioartic #datamesh #metadata #modernization #datasharing #datagovernance #ETL #datasilos #datagrowth #selfservice #compliance #artic #dataascode #branches #tags

Most companies use Hadoop for big data analytical workloads. The problem is, on-premises Hadoop deployments have failed to deliver business value after it is implemented. Over time, the high cost of operations and poor performance places a limitation on an organization’s ability to be agile. As a result, data platform teams are looking to modernize their Hadoop workloads to the data lakehouse.

In this video, learn about:

As enterprise data platforms look to operate at a more efficient level, they face the pressure to pivot their data management strategies. The increasing volume of data, demand for self-service analytics that meets compliance requirements, and complexity of data distribution channels are all factors to consider when making a business case.  In this video, we will cover the three-year Total Economic Impact™ of the data lakehouse and quantifiable benefits to productivity across all teams. You will learn about: - Key challenges organizations face with explosive data growth and data silos - Increasing team productivity and focusing more on high-value projects - Reducing data storage costs and retiring complicated ETL processes  

See all upcoming episodes: https://www.dremio.com/gnarly-data-wa...

Join the Dremio developer advocacy and engineering teams for an installment of Apache Iceberg Office Hours. In this video, we’ll have a brief Iceberg presentation on Hidden Partitioning and Partitioning transforms in Iceberg and then lots of dedicated time for Q&A on the presented topic or any other questions or guidance you’re looking for help on in learning about Apache Iceberg or architecting your data lakehouse around Apache Iceberg.  Examples of questions you can come to ask: How can I optimize my Iceberg tables for my different use cases? What tools will best handle my ETL job to write to Iceberg? How can I control access to my Iceberg tables? How can I convert data from X into an Iceberg table? How can I get started with Iceberg in Databricks?  #datalakehouse #analytics #datawarehouse #datalake #opendatalakehouse #gnarlydatawaves #apacheiceberg #dremio #tableau #bestpractices #dashboards #partitionevolution #metadata #icebergpartitioning #partitiontransforms #officehours #iceberg

Tableau is a visual analytics platform that helps more people in organizations see and understand their data. Dremio helps Tableau users accelerate access to data, including cloud data lakes, and it can dramatically improve query performance, delivering analytics for every data consumer at interactive speed.   In this video, we'll cover: - how the Dremio open data lakehouse connects Tableau users directly to data lake storage and other data repositories, 

- how reflections accelerate query performance for ad hoc analysis and interactive dashboards, and 

- how the Dremio semantic layer extends self

-service capabilities beyond the visualization layer, so anyone can join and query data easily.  

VIDEO ON YOUTUBE: https://www.youtube.com/watch?v=8fzYLgKHIj0

#datalakehouse #analytics #datawarehouse #datalake #opendatalakehouse #gnarlydatawaves #apacheiceberg #shorts #dremio #tableau #bestpractices #dashboards #optimizing #selfservice

Iceberg has been gaining wide adoption in the industry as the defacto open standard for data lakehouse table formats. Join us as we help you learn the options and strategies you can employ when migrating tables from Delta Lake to Apache Iceberg. We’ll cover:

PRESENTATION ON YOUTUBE: https://youtu.be/11p3AaPduos

Apache Iceberg FAQ: https://www.dremio.com/blog/apache-iceberg-faq/