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Rockset Includes Microsoft Azure Data Connectors to its Real-Time Analytics Platform

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Real-time analytics firm, Rockset will now be able to support the data ingestion from Azure Blob Storage, Azure Event Hubs, and Azure Service Bus. Instant data from the Azure data lake or event stream can be ingested, transformed, and analyzed without the need for complex real-time data pipelines.

Rockset’s Co-founder and CEO, Venkat Venkataramani, stated that “Companies are recognizing that they cannot build a data-driven culture relying on batch-based analytics and BI alone. There is too much latency at every step – finding the data, ingesting it, querying it and representing it. In an age of lightning-fast consumer apps such as Instagram, users won’t tolerate excruciatingly slow analytics experiences. Not your customers, nor even your internal employees. Azure has a strong public cloud presence, and with this release we are making real-time analytics more accessible and affordable for all Azure customers.”

Quicker analysis of data will be provided by staying in sync with the data sources such as event streams, databases, and lakes. There are no batch ETL processes required because the platform allows continuous data intake and analysis.

Rockset is a data warehouse that liberates developers from conventional data pipelines and replaces them with cloud-scale real-time analytics. Rockset maintains every field for sub-second search, aggregations, and joins, unlike typical warehouses. Azure customers can use managed connectors to connect to popular Azure and Microsoft services like Azure Blob Storage, Azure Event Hubs, and Azure Service Bus to sync their data.

With Amazon Web Services (AWS) and Google Cloud Platform (GCP) built-in connections that are completely maintained as part of its cloud platform, Rockset already interacts with many data lakes, event streams, and transactional databases. The new announcement also helps in eliminating the need for managing and building complicated data pipelines or using separate ETL tools. Live search, aggregation, and data integration from various data sources can be done using the data connectors.

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