Business Software for Apache Phoenix

The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. Whether you're new to programming or an experienced developer, it's easy to learn and use Python. Python can be easy to pick up whether you're a first-time programmer or you're experienced with other languages. The following pages are a useful first step to get on your way to writing programs with Python! The community hosts conferences and meetups to collaborate on code, and much more. Python's documentation will help you along the way, and the mailing lists will keep you in touch. The Python Package Index (PyPI) hosts thousands of third-party modules for Python. Both Python's standard library and the community-contributed modules allow for endless possibilities.

The Apache Hive data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage. A command line tool and JDBC driver are provided to connect users to Hive. Apache Hive is an open source project run by volunteers at the Apache Software Foundation. Previously it was a subproject of Apache® Hadoop®, but has now graduated to become a top-level project of its own. We encourage you to learn about the project and contribute your expertise. Traditional SQL queries must be implemented in the MapReduce Java API to execute SQL applications and queries over distributed data. Hive provides the necessary SQL abstraction to integrate SQL-like queries (HiveQL) into the underlying Java without the need to implement queries in the low-level Java API.

Trino is a query engine that runs at ludicrous speed. Fast-distributed SQL query engine for big data analytics that helps you explore your data universe. Trino is a highly parallel and distributed query engine, that is built from the ground up for efficient, low-latency analytics. The largest organizations in the world use Trino to query exabyte-scale data lakes and massive data warehouses alike. Supports diverse use cases, ad-hoc analytics at interactive speeds, massive multi-hour batch queries, and high-volume apps that perform sub-second queries. Trino is an ANSI SQL-compliant query engine, that works with BI tools such as R, Tableau, Power BI, Superset, and many others. You can natively query data in Hadoop, S3, Cassandra, MySQL, and many others, without the need for complex, slow, and error-prone processes for copying the data. Access data from multiple systems within a single query.

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

NoSQL is a domain-specific programming language used for accessing, managing, and manipulating non-tabular databases. A NoSQL (originally referring to "non-SQL" or "non-relational") database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but the name "NoSQL" was only coined in the early 21st century, triggered by the needs of Web 2.0 companies. NoSQL databases are increasingly used in big data and real-time web applications.NoSQL systems are also sometimes called Not only SQL to emphasize that they may support SQL-like query languages or sit alongside SQL databases in polyglot-persistent architectures. Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages.

Use Apache HBase™ when you need random, realtime read/write access to your Big Data. This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Automatic failover support between RegionServers. Easy to use Java API for client access. Thrift gateway and a REST-ful Web service that supports XML, Protobuf, and binary data encoding options. Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX.

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures. A wide variety of companies and organizations use Hadoop for both research and production. Users are encouraged to add themselves to the Hadoop PoweredBy wiki page. Apache Hadoop 3.3.4 incorporates a number of significant enhancements over the previous major release line (hadoop-3.2).

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.

Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault-tolerant with tunable reliability mechanisms and many failovers and recovery mechanisms. It uses a simple extensible data model that allows for online analytic applications. The Apache Flume team is pleased to announce the release of Flume 1.8.0. Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming event data.

Salesforce Data Cloud is a real-time data platform designed to unify and manage customer data from multiple sources across an organization, enabling a single, comprehensive view of each customer. It allows businesses to collect, harmonize, and analyze data in real time, creating a 360-degree customer profile that can be leveraged across Salesforce’s various applications, such as Marketing Cloud, Sales Cloud, and Service Cloud. This platform enables faster, more personalized customer interactions by integrating data from online and offline channels, including CRM data, transactional data, and third-party data sources. Salesforce Data Cloud also offers advanced AI gents and analytics capabilities, helping organizations gain deeper insights into customer behavior and predict future needs. By centralizing and refining data for actionable use, Salesforce Data Cloud supports enhanced customer experiences, targeted marketing, and efficient, data-driven decision-making across departments.

As a business leader, you need to trust your data and be 100% certain that it’s well-governed, compliant, and accurate. Including all data, in all sources, and in all locations, without limitations. Understand your data assets. Audit for risk, compliance, and quality in support of your project. Catalog a complete data inventory across all sources and data types, creating a shared understanding of your data assets. Run a one-time, fast, affordable, and accurate audit of your data. PCI, PII, and PHI audits are fast, accurate, and complete. As a service, with no software to purchase. Measure and audit data quality and data duplication across all of your enterprise data assets, cloud-native and on-premises. Comply with global data privacy regulations at scale. Discover, classify, track, trace and audit privacy compliance. Monitor PII/PCI/PHI data propagation and automate DSAR compliance processes.