Overview

SingleStore's Data Integration platform offers a native, high-performance solution for ingesting and processing data in real-time. Designed to eliminate the need for additional ETL tooling, it supports zero-ETL ingestion from various sources, including Kafka, Amazon S3, HDFS, and Apache Iceberg tables. This capability allows organizations to streamline their data workflows and achieve faster insights without the complexity of traditional ETL processes. The platform's native integration with Apache Iceberg enables seamless data ingestion and writing, facilitating low-latency analytics on lakehouse data. Additionally, it supports Change Data Capture (CDC) from MySQL and MongoDB, ensuring that data changes are captured and reflected in real-time. By automating schema evolution and updates, SingleStore ensures that changes in source systems are accurately mirrored in the database without manual intervention. With SingleStore's Data Integration platform, organizations can reduce or eliminate ETL tooling and data processing costs, achieving up to 50% lower total cost of ownership. The platform's robust, scalable, and highly performant pipelines support fully distributed workloads, making it suitable for modern, data-intensive applications.

Features and Capabilities

• Zero-ETL Ingestion: Directly ingest data from sources like Kafka, Amazon S3, HDFS, and Apache Iceberg tables without the need for additional ETL tools.
• Real-Time Data Processing: Support for continuous data ingestion at high throughput, enabling real-time analytics and decision-making.
• Change Data Capture (CDC): Capture and reflect data changes from MySQL and MongoDB in real-time, ensuring up-to-date information in the database.
• Native Apache Iceberg Integration: Seamless integration with Apache Iceberg for low-latency analytics on lakehouse data, including support for schema evolution and updates.
• Cost Efficiency: Reduce or eliminate ETL tooling and data processing costs, achieving up to 50% lower total cost of ownership.
• Scalable Pipelines: Robust, scalable, and highly performant pipelines that support fully distributed workloads, suitable for modern applications.
• Support for Multiple Data Formats: Ingest and process data in various formats, including JSON, Avro, Parquet, and CSV.
• Automated Schema Management: Automatically manage schema evolution and updates, ensuring consistency between source systems and the database.
• Integration with Jupyter Notebooks: Utilize Jupyter Notebooks for exploring and visualizing data, facilitating data analysis and experimentation.