Bringing massively scalable cloud analytics to everyone
The fastest way to make your SkySQL databases available to developers, analysts, data scientists and engineers, via a popular modern analytics layer, Spark SQL. Integrate any other data, structured or unstructured, from sources like S3 and formats like Parquet, Avro, CVS, etc. Harness cloud economics with the separation of compute and storage for cost efficient, serverless use while offering massive analysis capacity for interactive and collaborative analytics.
SkySQL Notebook powered by Apache Zeppelin empowers your cross-functional teams to quickly spin up and perform data-driven, interactive data analytics with SQL, Python, Java, Scala and R.
Rapidly perform bulk ingestion, join and analyze disparate data sources with different formats including JSON, Hive, ORC, Avro, Parquet, CSV and many others without ETL.
Quickly and affordably scale analytics using Apache Spark on Kubernetes orchestrated compute pools (clusters) as a distributed SQL engine. Pay only for what you use without the need for DBA or IT skills.
Embed SQL queries and ad hoc analytics into Spark programs, seamlessly switching back and forth between APIs and programming languages, returning results as structured datasets.
Inherits all MariaDB and SkySQL connectivity plus Apache Spark BI reporting and visualization tool vendor support via JDBC and ODBC drivers.
MariaDB SkySQL with serverless analytics powered by Apache Spark SQL empowers cross-functional teams to rapidly and affordably start small and scale up analytics projects covering a range of use cases across various industries.
Analyze millions of quotes for trading in tens of milliseconds to estimate price point, profitability and risk.
Analyze disparate and diverse data across multiple database instances and increase query performance to support massive datasets.
Analyze customer behavior for retail use cases such as cross-sell/up-sell, customer loyalty and churn.
Analyze ERP and IoT data to predict points of component and systems failure thereby increasing uptime and reducing maintenance costs.
Analyze large amounts of data with standard SQL, including joins with other tables (row or columnar).
Use Spark connectors and interpreters to ingest data and publish machine learning results for interactive, ad hoc analysis.
Bring together different skills sets and programming languages into a single interactive and collaborative environment with multiple backends.