How to Make the Most of Apache Iceberg for Your Cloud Data Lakehouse

When data storage evolved from siloed databases to data warehouses, it empowered organizations to perform analytics across a vast volume of structured data. However, as the business landscape has evolved, data formats, too, have grown beyond structured data. Today, more sources such as the Internet of Things (IoT)/edge computing devices, social media and customer service chatbots produce a vast volume of semi-structured and unstructured data that can be mined for valuable insights.

While data warehouses were not equipped to handle semi-structured and unstructured data, business teams now needed a way to analyze the data being collected in these new formats for more comprehensive insights.

Data lakes emerged to solve this problem by allowing data teams to ingest and store all data formats — structured, semi-structured, and unstructured — in the raw form, without transformation. While data lakes allowed the storage of large data sets in multiple formats, new challenges around data governance quickly arose. Data lakes quickly became ‘swamps’ because they did not have essential capabilities, such as catalog, schema and change management or the partition complexity to organize, store, track and access data. The insights were in there somewhere, but the right data was almost impossible to find and use.

Challenges aside, data teams are under immense pressure to deliver value for the business from data analytics. They are tasked with transforming the deluge of structured and unstructured data streaming from multiple sources into advanced, business-ready analytics and insights that can give the business a competitive edge.

As a result, an even more advanced storage option — the data lakehouse — has emerged to enable advanced analytics and artificial intelligence (AI) applications with large, multi-format data sets.

The main challenge with data lakes was the lack of data organization. Lakehouses, powered by open table formats, emerged to combine the structure, quality and governance of data warehouses with the flexibility and scale of data lakes.

Open table formats simplify data management over an extended period and offer numerous advantages, including enhanced performance, reliability and scalability. They add a layer of metadata on top of file formats such as ORC, Parquet and Avro that store multi-format data in the lake. These table formats explicitly define a table, its metadata and the files that compose it. They also enable a standardized way to organize and manage the data in the lake for advanced analytics and machine learning (ML) projects. In other words, they turn the potential data swamp into an efficient and accessible data lakehouse.

Open table formats like Delta Lake, Apache Iceberg and Apache Hudi have emerged as the most popular open table storage formats to manage massive data sets and provide efficient query performance for high volume data engineering workloads in the data lakehouse. They have added more meaning to data and drastically improved analytics and AI project outcomes. 

Apache Iceberg has emerged as a powerful open table format, providing a robust layer of abstraction over traditional data lakes. It offers a stable foundation for storing vast data sets and performing large-scale, high-performance data management operations with a flexible and powerful schema evolution mechanism.  The advanced metadata capabilities of Iceberg help manage vast multi-format data sets for high-performance analytics and AI projects.

Iceberg adoption is growing across all major cloud data ecosystems, from Snowflake to AWS and Microsoft Fabric to Databricks (with the acquisition of Tabular). In addition to its technical capabilities, data engineers prefer it because an open community and open-source catalog drives it, and it is free and customizable. This makes it a vendor-independent solution, meaning you can easily move across different applications simultaneously while avoiding vendor lock-in.

As a data engineer, you want to ensure your multi-format data is organized and available in your cloud data lakehouse. That way, your business users can get timely reports, and your data scientists can build advanced LLMs for analytics and GenAI apps. 

Every lakehouse needs a strong data management foundation to deliver optimal performance with cloud analytics. The right connectors are crucial to ensure you can connect, read and write data seamlessly between data sources and the lakehouse. They help extract data from an application system and write it to the lakehouse so you can leverage it for analysis. Similarly, you need to prepare data for analysis by reading the data from the open tables and enriching it before writing it to your chosen lake or warehouse format.

Too many data engineers still struggle with hand-coding for building their data engineering pipelines to build these connectors. Such manual pipelines are not enterprise-grade or reusable but add to cost, complexity and sometimes even security vulnerabilities.

Others sometimes choose limited solutions from point solution cloud vendors for ad-hoc or stop-gap solutions. These tools may seem quick and low-cost in the short term, but they are problematic as the capabilities tend to extend only as far as their own platforms. Layering more point solutions into the data stack could also add to technical debt and integration challenges as you scale.

Modern cloud data management requirements for modern enterprises must extend beyond any single PaaS to a multi-cloud strategy and deployment model. You need a cloud data integration solution that can future-proof your data analytics initiatives. Are you wondering how important this is? Remember on-premises warehouses, Hadoop, big data, Spark and the shift to the cloud.

The Informatica Intelligent Data Management Cloud (IDMC) provides an intelligent, agnostic and comprehensive data management platform for cloud data lakehouses (as well as data warehouses and data lakes) with best-of-breed data integration, data quality and metadata management capabilities. With IDMC, you can construct streamlined, automated, no-code pipelines without expensive, inefficient approaches like manual coding, piecing together individual products or solutions restricted to specific ecosystems.

CLAIRE, the FinOps-enabled AI engine for IDMC, supports the reading, writing and processing of data within an Apache Iceberg table. It also helps optimize costs by automatically selecting the optimal mode (i.e., Native, Spark extract, load, transform (ELT), SQL ELT) based on the data pipeline and use case.

To address modern use cases driven by open table formats, Informatica is launching a new native connector for open table formats. The new open table connector currently supports the Iceberg format and will eventually support Delta Lake and Apache Hudi as well.

The new open table format connectors from Informatica will help you seamlessly connect to your data in Iceberg format for data ingestion and integration to drive mission-critical data management use cases.

IDMC users get automatic access to the new connector for the Iceberg table format, with Support for S3 as storage and AWS glue as catalog. Sign up and start seamlessly reading from or writing to Iceberg table formats using the new open table format connector from Informatica. Upcoming releases of this connector for open format tables will also support ADLS Gen2, OneLake, Google cloud storage for storage layer, Hive metastore and REST-based catalogs.

Try the new connector for Apache Iceberg open table format now.