Business Software for definity

Your cloud data platform. Secure and easy access to any data with infinite scalability. Get all the insights from all your data by all your users, with the instant and near-infinite performance, concurrency and scale your organization requires. Seamlessly share and consume shared data to collaborate across your organization, and beyond, to solve your toughest business problems in real time. Boost the productivity of your data professionals and shorten your time to value in order to deliver modern and integrated data solutions swiftly from anywhere in your organization. Whether you’re moving data into Snowflake or extracting insight out of Snowflake, our technology partners and system integrators will help you deploy Snowflake for your success.

Dataproc makes open source data and analytics processing fast, easy, and more secure in the cloud. Build custom OSS clusters on custom machines faster. Whether you need extra memory for Presto or GPUs for Apache Spark machine learning, Dataproc can help accelerate your data and analytics processing by spinning up a purpose-built cluster in 90 seconds. Easy and affordable cluster management. With autoscaling, idle cluster deletion, per-second pricing, and more, Dataproc can help reduce the total cost of ownership of OSS so you can focus your time and resources elsewhere. Security built in by default. Encryption by default helps ensure no piece of data is unprotected. With JobsAPI and Component Gateway, you can define permissions for Cloud IAM clusters, without having to set up networking or gateway nodes.

Version control, quality assurance, documentation and modularity allow data teams to collaborate like software engineering teams. Analytics errors should be treated with the same level of urgency as bugs in a production product. Much of an analytic workflow is manual. We believe workflows should be built to execute with a single command. Data teams use dbt to codify business logic and make it accessible to the entire organization—for use in reporting, ML modeling, and operational workflows. Built-in CI/CD ensures that changes to data models move appropriately through development, staging, and production environments. dbt Cloud also provides guaranteed uptime and custom SLAs.

Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.

Amazon EMR is the industry-leading cloud big data platform for processing vast amounts of data using open-source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. With EMR you can run Petabyte-scale analysis at less than half of the cost of traditional on-premises solutions and over 3x faster than standard Apache Spark. For short-running jobs, you can spin up and spin down clusters and pay per second for the instances used. For long-running workloads, you can create highly available clusters that automatically scale to meet demand. If you have existing on-premises deployments of open-source tools such as Apache Spark and Apache Hive, you can also run EMR clusters on AWS Outposts. Analyze data using open-source ML frameworks such as Apache Spark MLlib, TensorFlow, and Apache MXNet. Connect to Amazon SageMaker Studio for large-scale model training, analysis, and reporting.

Unlock insights from all your data and build artificial intelligence (AI) solutions with Azure Databricks, set up your Apache Spark™ environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn. Azure Databricks provides the latest versions of Apache Spark and allows you to seamlessly integrate with open source libraries. Spin up clusters and build quickly in a fully managed Apache Spark environment with the global scale and availability of Azure. Clusters are set up, configured, and fine-tuned to ensure reliability and performance without the need for monitoring. Take advantage of autoscaling and auto-termination to improve total cost of ownership (TCO).

SQL is a domain-specific programming language used for accessing, managing, and manipulating relational databases and relational database management systems.