Microsoft has announced the general availability of new Azure analytics services, including Azure Data Explorer (ADX) and Azure Data Lake Storage (ADLS) Gen2.
According to Microsoft, ADX provides a fully managed database service for real-time analysis on large volumes of streaming data, enabling users to explore and discover insights from data that would otherwise require signification pre-processing—and potentially infrastructure investment if the work is done on premise.
ADLS Gen2 combines the scalability, cost-effectiveness, security model, and capabilities of Azure Blog Storage with a high-performance file system that is built for analytics and compatible with the Hadoop Distributed File System.
The new services were announced in multiple Microsoft blog posts.
Announcing ADX, Julia White, corporate vice president, Microsoft Azure, said it “simplifies ad hoc and interactive analysis over telemetry, time-series, and log data.” ADX “powering other Azure services like Log Analytics, App Insights, Time Series Insights, is useful to query streaming data to identify trends, detect anomalies, and diagnose problems.
ADX is a fully managed data analytics service for real-time analysis on large volumes of streaming data, noted Jurgen Willis, director of product management, Azure Engineering, noting “ADX is capable of querying 1 billion records in under a second with no modification of the data or metadata required. ADX also includes native connectors to Azure Data Lake Storage, Azure SQL Data Warehouse, and Power BI and comes with an intuitive query language so that customers can get insights in minutes.”
According to Willis, “Designed for speed and simplicity, ADX is architected with two distinct services that work in tandem: The Engine and Data Management (DM) service. Both services are deployed as clusters of compute nodes (virtual machines) in Azure.”
Announcing the GA of Azure Data Lake Storage in a blog post, Arun Ulag
general manager, Engineering, said “In tackling the data explosion, companies need a place to manage all this data.”
Ulag explained that Power BI dataflows enable business analysts to create reusable ETL packages with point and click simplicity and can be configured to store data in the customer’s Azure Data Lake Storage instance, allowing business analysts, data engineers, and data scientists to collaborate without the need to move the data around.
“Business analysts can seamlessly operate on data stored in Azure Data Lake Storage, taking advantage of its scale, performance, and security. Meanwhile, data engineers and data scientists can extend insights with advanced analytics and AI from complementary Azure Data Services like Azure Machine Learning, Azure Databricks, and Azure SQL Data Warehouse,” wrote Ulag.