Best Data Pipeline Software for Amazon S3 - Page 2

​Observo AI is an AI-native data pipeline platform designed to address the challenges of managing vast amounts of telemetry data in security and DevOps operations. By leveraging machine learning and agentic AI, Observo AI automates data optimization, enabling enterprises to process AI-generated data more efficiently, securely, and cost-effectively. It reduces data processing costs by over 50% and accelerates incident response times by more than 40%. Observo AI's features include intelligent data deduplication and compression, real-time anomaly detection, and dynamic data routing to appropriate storage or analysis tools. It also enriches data streams with contextual information to enhance threat detection accuracy while minimizing false positives. Observo AI offers a searchable cloud data lake for efficient data storage and retrieval.

​Onum is a real-time data intelligence platform that empowers security and IT teams to derive actionable insights from data in-stream, facilitating rapid decision-making and operational efficiency. By processing data at the source, Onum enables decisions in milliseconds, not minutes, simplifying complex workflows and reducing costs. It offers data reduction capabilities, intelligently filtering and reducing data at the source to ensure only valuable information reaches analytics platforms, thereby minimizing storage requirements and associated costs. It also provides data enrichment features, transforming raw data into actionable intelligence by adding context and correlations in real time. Onum simplifies data pipeline management through efficient data routing, ensuring the right data is delivered to the appropriate destinations instantly, supporting various sources and destinations.

​Tenzir is a data pipeline engine specifically designed for security teams, facilitating the collection, transformation, enrichment, and routing of security data throughout its lifecycle. It enables users to seamlessly gather data from various sources, parse unstructured data into structured formats, and transform it as needed. It optimizes data volume, reduces costs, and supports mapping to standardized schemas like OCSF, ASIM, and ECS. Tenzir ensures compliance through data anonymization features and enriches data by adding context from threats, assets, and vulnerabilities. It supports real-time detection and stores data efficiently in Parquet format within object storage systems. Users can rapidly search and materialize necessary data and reactivate at-rest data back into motion. Tension is built for flexibility, allowing deployment as code and integration into existing workflows, ultimately aiming to reduce SIEM costs and provide full control.

Improving the experience, efficiency, and profitability of streaming video requires data. Datazoom enables video publishers to better operate distributed architectures through centralizing, standardizing, and integrating data in real-time to create a more powerful data pipeline and improve observability, adaptability, and optimization solutions. Datazoom is a video data platform that continually gathers data from endpoints, like a CDN or a video player, through an ecosystem of collectors. Once the data is gathered, it is normalized using standardized data definitions. This data is then sent through available connectors to analytics platforms like Google BigQuery, Google Analytics, and Splunk and can be visualized in tools such as Looker and Superset. Datazoom is your key to a more effective and efficient data pipeline. Get the data you need in real-time. Don’t wait for your data when you need to resolve an issue immediately.

Find out why the heavy hitters are using the Crux external data automation platform to scale external data integration, transformation, and observability without increasing headcount. Our cloud-native data integration technology accelerates the ingestion, preparation, observability and ongoing delivery of any external dataset. The result is that we can ensure you get quality data in the right place, in the right format when you need it. Leverage automatic schema detection, delivery schedule inference, and lifecycle management to build pipelines from any external data source quickly. Enhance discoverability throughout your organization through a private catalog of linked and matched data products. Enrich, validate, and transform any dataset to quickly combine it with other data sources and accelerate analytics.

BigBI enables data specialists to build their own powerful big data pipelines interactively & efficiently, without any coding! BigBI unleashes the power of Apache Spark enabling: Scalable processing of real Big Data (up to 100X faster) Integration of traditional data (SQL, batch files) with modern data sources including semi-structured (JSON, NoSQL DBs, Elastic, Hadoop), and unstructured (Text, Audio, video), Integration of streaming data, cloud data, AI/ML & graphs

CData Sync is a universal data pipeline that delivers automated continuous replication between hundreds of SaaS applications & cloud data sources and any major database or data warehouse, on-premise or in the cloud. Replicate data from hundreds of cloud data sources to popular database destinations, such as SQL Server, Redshift, S3, Snowflake, BigQuery, and more. Configuring replication is easy: login, select the data tables to replicate, and select a replication interval. Done. CData Sync extracts data iteratively, causing minimal impact on operational systems by only querying and updating data that has been added or changed since the last update. CData Sync offers the utmost flexibility across full and partial replication scenarios and ensures that critical data is stored safely in your database of choice. Download a 30-day free trial of the Sync application or request more information at www.cdata.com/sync

Build your data pipelines like Lego blocks with Data Taps. Add new metrics layers, zoom in, and investigate with real-time streaming SQL. Build with others, share and consume data, globally. Refine and update without hassle. Use multiple models/schemas during schema evolution. Built to scale with AWS Lambda and S3.