Microsoft today announced the general availability of Azure Data Explorer (ADX) and Azure Data Lake Storage Gen2 (ADLS Gen2) — two services it says will afford Azure customers greater flexibility in managing unstructured data, or data generated from interactions on the web, software-as-a-service apps, social media, mobile apps, and internet of things devices. The company also launched a preview of Mapping Data Flow in Azure Data Factory, a visual, no-code app that enables developers to build “data transformations.”

“We always strive to make it very easy for IT staff to adopt analytics and for line-of-business people to utilize and deliver powerful insights using beautiful products,” said John Chirapurath, general manager of Azure data, blockchain, and AI at Microsoft.

ADLS Gen2 provides a Hadoop Distributed File System-compatible repository for storing both structured and unstructured data, along with an Azure Blob File System driver that allows files and folders to be distinctly addressed on the server side.

Meanwhile, ADX is purpose-built for speedy big data analytics. Microsoft claims ADX can analyze 1 billion records of streaming data per second while leaving the data and its metadata in its original state — letting engineers derive insights without having to preprocess that data. (The company pointed to a January study conducted by GigaOm that found Azure SQL Data Warehouse outperforms competing platforms like Google BigQuery in TPC-H benchmark tests by up to 14 times.)

These and other features recently convinced Newell Brands, BookBeat, and Heathrow Airport to move their workloads to Azure, Julie White, corporate vice president for Microsoft Azure, said in a blog post.

“This industry-leading price-performance extends to the rest of our analytics stack. This includes Azure Data Lake Storage, our cloud data storage service, and Azure Databricks, our big data processing service,” she said. “Only Azure provides the most comprehensive set of analytics services, from data ingestion to storage to data warehousing to machine learning and business intelligence.”