As the variety and the volume of data explodes, data science teams face various hurdles in managing it effectively. This requires new thinking and unique approaches that will streamline management of large data sets.
This is where table formats such as Apache Iceberg come into play – they provide a robust and reliable open-source table format that simplifies data processing on large datasets stored in data lakes. Most importantly they are fast, efficient and reliable at any scale.
Choosing the right table format can help organizations realize the full potential of their data.
While Snowflake’s internal table formats make data storage easier to manage, some organizations need to store data outside of Snowflake in open-source formats, often due to regulatory or other restrictions.
Iceberg tables that combine unique Snowflake capabilities with Apache Iceberg open-source projects can help solve these challenges. They allow customers to bring even more data to Snowflake to power their new use cases and solve three key challenges with large data sets they are facing: control, cost and interoperability.
BlueCloud’s data engineering team joined forces with Snowflake to help organizations unlock insights with Snowflake Iceberg tables. In this article, we’ll dive into the benefits of Snowflake Iceberg Tables, highlight the key role BlueCloud has in building data lakes with Iceberg tables, and walk you through the steps to build open data lake house with Snowflake Iceberg Tables. Additionally, you’ll discover how to use Snowflake Cortex AI for sentiment analysis on Iceberg Tables, bringing advanced analytics to your data.
The Key to an Organized Data Lake Lies in Effective Table Formats
Data lakes are designed for storing massive amounts of unstructured and semi-structured data just as it is, letting organizations dive into and analyze data from all over.
But sometimes, the files in a data lake lack the structure needed for smooth data management tasks like pruning, time travel, or updating schemas.
Table formats can help solve these challenges by adding metadata that brings structure and makes data lakes behave more like SQL tables, defining a table's layout, history, and file structure. Formats like Iceberg also ensure ACID compliance, allowing different applications to work on the same data at once without conflicts.
These scalable formats are suitable for large datasets in distributed storage systems like HDFS or cloud storage and allow for efficient storage and analytical queries, with major formats including Apache Iceberg, Apache Hudi, and Delta Lake.
Iceberg Tables: Combining Unique Snowflake Capabilities with the Apache Iceberg
Imagine you wanted to streamline your data lakes with Snowlfake tables but also wanted those tables to use your own data storage or open-source formats? Iceberg Tables is just the right solution you have been searching for.
At its core, Iceberg tables are Snowflake tables with open formats and customer-managed cloud storage. While open-source products often introduce features that feel separate, Iceberg Tables integrate all the elements, providing seamless user experience.
Iceberg Tables, a scalable and optimized data format that enhances data accessibility, partitioning, and versioning, are critical components for large datasets and analytics solutions. They bring Snowflake's easy platform management and great performance to data stored externally in the open-source Apache Iceberg format.
Snowflake’s flexibility with Iceberg tables, from data sharing and version control to advanced security and text analytics capabilities, underscores the platform’s robust support for complex data management and AI-driven insights.
Why Apache Iceberg?
Snowflake Data Cloud now supports Apache Iceberg in public preview with Iceberg Tables.
Apache Iceberg is an open-table format that helps you handle huge analytical datasets. It gives you a high-performance table setup that brings the best of traditional databases—like SQL querying, ACID compliance, and partitioning—to your data files.
Apache Iceberg is ideal for existing data lakes that you cannot, or choose not to, store in Snowflake. Think of Iceberg as a lens that lets you see and manage multiple data files as one cohesive table. The big win here is Iceberg’s ability to handle massive data sets efficiently and with top-tier optimization.
Connecting the Dots with BlueCloud: Unlocking Insights through Snowflake Cortex AI and Iceberg Tables
BlueCloud teamed up with Snowflake to guide users in creating Iceberg Tables in Snowflake, enabling advanced sentiment analysis with Cortex AI.
"BlueCloud is the implementation partner - the golden thread that ties all of the key components of the data stack together with the goal to deliver data and analytics solutions tailored to customer needs."
Watch the webinar unlocking Geospatial, Iceberg and Snowflake Cortex AI capabilities with BlueCloud, Fivetran and Coalesce to learn more about how BlueCloud can help you create and govern Iceberg Tables for an open data Lakehouse architecture, paving the way for scalable data governance and advanced analytics.
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