{"id":1545,"date":"2018-11-05T07:00:17","date_gmt":"2018-11-05T12:00:17","guid":{"rendered":"https:\/\/solutionsreview.com\/data-management\/?p=1545"},"modified":"2018-12-05T16:49:29","modified_gmt":"2018-12-05T21:49:29","slug":"the-10-best-data-management-solutions-for-analytics-for-2019","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/data-management\/the-10-best-data-management-solutions-for-analytics-for-2019\/","title":{"rendered":"The 10 Best Data Management Solutions for Analytics for 2019"},"content":{"rendered":"

\"The<\/p>\n

The marketplace for the best data management solutions for analytics is mature and crowded with excellent software tools for a variety of use cases, verticals, deployment methods and budgets.\u00a0Two sub-markets have branched off of the overarching data management space over the last few years. These include tools for metadata management as well as master data management. Although not linear in functional use, the categories are inter-related in relation to the big data analytics process.<\/p>\n

Modern data management products offer a broad spectrum of capabilities used to analyze data from disparate and increasingly diverse sources. Traditional data warehousing techniques are slowly being phased out with the adoption of data lakes, and cloud connectivity has emerged as a differentiating factor in a growing number of deployments.\u00a0In an attempt to assist you with what can become a daunting task of selecting the right product, these are the top-10 best data management solutions for analytics for 2019.<\/p>\n

\"\"<\/p>\n

Amazon Web Services (AWS) offers Amazon Redshift, a fully managed, petabyte-scale data warehouse that analyzes data using an organization\u2019s existing analytic software. Redshift\u2019s data warehouse architecture allows users to automate common administrative tasks associated with provisioning, configuring, and monitoring cloud data warehousing. Backups to Amazon S3 are continuous, incremental, and automatic.<\/p>\n

For an even deeper breakdown of each provider listed, consult our vendor map<\/a><\/strong>.<\/b><\/p>\n

\"\"<\/p>\n

Cloudera offers a data storage and processing platform based on the Apache Hadoop ecosystem, as well as a proprietary system and data management tools for design, deployment, operations and production management. Cloudera differentiates itself from other Hadoop distribution vendors by continuing to invest in specific capabilities, such as improvements to Cloudera Navigator (which provides metadata management, lineage, and auditing), while at the same time keeping up with the Hadoop open-source project.<\/p>\n

For an even deeper breakdown of each provider listed, consult our vendor map<\/a><\/strong>.<\/b><\/p>\n

\"\"<\/p>\n

The Hortonworks Data Platform is a secure and open source Apache Hadoop distribution based on a centralized architecture (YARN). Hortonworks provides users the ability to run the platform in the data center as well as on the public cloud of choice. The tool includes a range of processing engines that enable users to interact with data in multiple ways, and applications for big data analytics can interact with data from batch to interactive SQL or low latency access with NoSQL.<\/p>\n

For an even deeper breakdown of each provider listed, consult our vendor map<\/a><\/strong>.<\/b><\/p>\n

\"MapR\"<\/p>\n

The MapR Converged Data Platform integrates Hadoop, Spark, and Apache Drill with real-time database capabilities, global event streaming, and enterprise storage. The product is available in two editions, and MapR supports dozens of open source projects that use industry-standard APIs. MapR is also the only Hadoop provider that supports multiple versions of key Apache projects so organizations can update environments at their own pace.<\/p>\n

For an even deeper breakdown of each provider listed, consult our vendor map<\/a><\/strong>.<\/b><\/p>\n

\"MarkLogic\"<\/p>\n

MarkLogic offers an operational and transactional enterprise NoSQL database that is designed to integrate, store, manage, and search for data. Organizations can ingest structured and unstructured data with a flexible data model that adapts to changing data. It also natively stores JSON, XML, text, and geospatial data. MarkLogic\u2019s Universal Index enables users to search across all data, and APIs enable application development and deployment. The database has ACID transactions, scalability and elasticity, and certified security as well.<\/p>\n

For an even deeper breakdown of each provider listed, consult our vendor map<\/a><\/strong>.<\/b><\/p>\n

\"MemSQL\"<\/p>\n

MemSQL is a distributed database that delivers performance for transactional and analytical workloads with familiar relational data structures. The tool allows users to ingest millions of events per day with ACID transactions while also analyzing billions of rows of data in relational SQL, JSON, Geospatial, and Full-Text Search formats. ANSI SQL provides ultra-fast query response across both live and historical data as well, and the system balances data and queries across a cluster of cloud instances or commodity hardware<\/p>\n

For an even deeper breakdown of each provider listed, consult our vendor map<\/a><\/strong>.<\/b><\/p>\n

\"Pivotal\"<\/p>\n

Pivotal\u2019s data management product features a popular open-source framework, and all Greenplum contributions are part of the Greenplum Database project and share the same database core, including the MPP architecture, analytic interfaces, and security. The solution offers integration with cloud data repositories and data lakes via external tables that provide access to data stored outside Greenplum as it if it were stored in regular database tables. Pivotal also includes business continuity add-ons like intelligent fault detection, incremental backup and disaster recovery.<\/p>\n

For an even deeper breakdown of each provider listed, consult our vendor map<\/a><\/strong>.<\/b><\/p>\n

\"SAP\"<\/a><\/p>\n

The SAP<\/a> Data Management Suite features four distinct solutions for a variety of use cases: SAP HANA, SAP Data Hub, SAP Cloud Platform Big Data Services, and SAP Enterprise Architecture Designer. The suite allows organizations to connect, analyze, govern, secure, and share data. SAP features an in-memory framework, and allows users to create a single, unified view of data with smart data integration. The tool works with local, private, and public clouds.<\/p>\n

For an even deeper breakdown of each provider listed, consult our vendor map<\/a><\/strong>.<\/b><\/p>\n

\"Snowflake\"<\/p>\n

Snowflake Computing offers a cloud data warehouse product built atop Amazon Web Services. The solution loads and optimizes data from virtually any source, both structured and unstructured, including JSON, Avro, and XML. Snowflake features broad support for standard SQL, and users can do updates, deletes, analytical functions, transactions, and complex joins as a result. The tool requires zero management and no infrastructure. The columnar database engine uses advanced optimizations to crunch data, process reports, and run analytics.<\/p>\n

For an even deeper breakdown of each provider listed, consult our vendor map<\/a><\/strong>.<\/b><\/p>\n

\"Teradata\"<\/a><\/p>\n

Teradata<\/a>\u2019s data management portfolio includes products and services in data warehousing, big data analytics, and marketing applications. The company offers what we consider to be the most pure database and data warehousing capabilities of any provider in the space. Teradata covers nearly every enterprise use case, and its ability to integrate with Hadoop and other data sources make it increasingly flexible.<\/p>\n

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\"Download<\/a><\/p>\n<\/div>\n\t\t<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"

The marketplace for the best data management solutions for analytics is mature and crowded with excellent software tools for a variety of use cases, verticals, deployment methods and budgets.\u00a0Two sub-markets have branched off of the overarching data management space over the last few years. These include tools for metadata management as well as master data […]<\/p>\n","protected":false},"author":23,"featured_media":1571,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[3],"tags":[11,438,465],"acf":[],"yoast_head":"\nThe 10 Best Data Management Solutions for Analytics for 2019<\/title>\n<meta name=\"description\" content=\"A 10-vendor compilation of the best data management solutions for analytics for 2019. 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