Systems | Development | Analytics | API | Testing

The BigQuery tables for Apache Iceberg storage engine offers autonomous storage optimizations, clustering, and high-throughput streaming ingestion.

Many enterprises face significant challenges when it comes to building data pipelines in AWS, particularly around data ingestion. As data from diverse sources continues to grow exponentially, managing and processing it efficiently in AWS is critical. Without these capabilities, it’s harder to analyze and get any meaning from your data.

The open data lakehouse is quickly becoming the standard architecture for unified multifunction analytics on large volumes of data. It combines the flexibility and scalability of data lake storage with the data analytics, data governance, and data management functionality of the data warehouse.

While data lakes make it easy to store and analyze a wide variety of data types, they can become data swamps without the proper documentation and governance. Until you solve the biggest data lake challenges — tackling exponential big data growth, costs, and management complexity — efficient and reliable data analytics will remain out of reach.

Businesses today rely heavily on data to inform decisions, predict trends, and optimize operations. However, more data volume and complexity has led to growing pressure to find scalable, cost-effective solutions for data storage while staying within IT budgets. Companies want to handle both structured and unstructured data efficiently, while supporting advanced data analysis and machine learning use cases.

Today’s enterprise networks are complex. Potential attackers have a wide variety of access points, particularly in cloud-based or multi-cloud environments. Modern threat hunters have the challenge of wading through vast amounts of data in an effort to separate the signal from the noise. That’s where a security data lake can come into play.