Best Data Management Software for PHEMI Health DataLab

Secure and manage the data lifecycle, from Edge to AI in any cloud or data centre. Operates on all major public clouds as well as the private cloud with a public experience everywhere. Integrates data management and analytics experiences across the entire data lifecycle. All environments are covered by security, compliance, migration, metadata management. Open source, extensible, and open to multiple data stores. Self-service analytics that is faster, safer, and easier to use. Self-service access to multi-function, integrated analytics on centrally managed business data. This allows for consistent experiences anywhere, whether it is in the cloud or hybrid. You can enjoy consistent data security, governance and lineage as well as deploying the cloud analytics services that business users need. This eliminates the need for shadow IT solutions.

Apache Hive™, a data warehouse software, facilitates the reading, writing and management of large datasets that are stored in distributed storage using SQL. Structure can be projected onto existing data. Hive provides a command line tool and a JDBC driver to allow users to connect to it. Apache Hive is an Apache Software Foundation open-source project. It was previously a subproject to Apache® Hadoop®, but it has now become a top-level project. We encourage you to read about the project and share your knowledge. To execute traditional SQL queries, you must use the MapReduce Java API. Hive provides the SQL abstraction needed to integrate SQL-like query (HiveQL), into the underlying Java. This is in addition to the Java API that implements queries.

Tableau, a comprehensive business intelligence (BI/analytics) solution, allows you to generate, analyze, and interpret business data. Tableau allows users to gather data from many sources, including spreadsheets, SQL databases and Salesforce. Tableau offers real-time visual analytics as well as an interactive dashboard that allows users to slice and dice data to make relevant insights and find new opportunities. Tableau allows users to customize the platform for different industry verticals such as communication, banking, and more.

Apache Ranger™, a framework that enables, monitors and manages comprehensive data security across Hadoop's platform, is called Apache Ranger. Ranger's goal is to provide complete security across the Apache Hadoop ecosystem. Apache YARN has made it possible to create a data lake architecture on Hadoop. Multi-tenant environments allow enterprises to run multiple workloads. Hadoop data security must evolve to support multiple use-cases for data access. It also provides a framework for central administration and monitoring of user access. All security-related tasks can be managed centrally through a UI or REST APIs using central security administration. Fine-grained authorization to perform a specific action or operation with a Hadoop component/tool. This is managed through a central admin tool. Standardize authorization methods across all Hadoop components. Enhanced support for different authorization methods, such as Role-based access control, etc.

Apache Hadoop is a software library that allows distributed processing of large data sets across multiple computers. It uses simple programming models. It can scale from one server to thousands of machines and offer local computations and storage. Instead of relying on hardware to provide high-availability, it is designed to detect and manage failures at the application layer. This allows for highly-available services on top of a cluster computers that may be susceptible to failures.

Apache Spark™, a unified analytics engine that can handle large-scale data processing, is available. Apache Spark delivers high performance for streaming and batch data. It uses a state of the art DAG scheduler, query optimizer, as well as a physical execution engine. Spark has over 80 high-level operators, making it easy to create parallel apps. You can also use it interactively via the Scala, Python and R SQL shells. Spark powers a number of libraries, including SQL and DataFrames and MLlib for machine-learning, GraphX and Spark Streaming. These libraries can be combined seamlessly in one application. Spark can run on Hadoop, Apache Mesos and Kubernetes. It can also be used standalone or in the cloud. It can access a variety of data sources. Spark can be run in standalone cluster mode on EC2, Hadoop YARN and Mesos. Access data in HDFS and Alluxio.