{"id":3946,"date":"2020-12-17T14:46:38","date_gmt":"2020-12-17T19:46:38","guid":{"rendered":"https:\/\/solutionsreview.com\/data-integration\/?p=3946"},"modified":"2020-12-17T15:12:59","modified_gmt":"2020-12-17T20:12:59","slug":"ascend-unified-data-engineering-gets-new-flex-code-data-connectors","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/data-integration\/ascend-unified-data-engineering-gets-new-flex-code-data-connectors\/","title":{"rendered":"Ascend Unified Data Engineering Gets New Flex-Code Data Connectors"},"content":{"rendered":"
\"Ascend

Source: Ascend.io<\/p><\/div>\n

Ascend.io announced major updates<\/a> to its Unified Data Engineering Platform with new flex-code data connectors, according to a press release on the company’s website. The Ascend Unified Data Engineering Platform’s flex-code data connectors act as a framework for data ingestion in Apache Spark that bridge connectivity between databases, data lakes, data warehouses, and APIs. The company is adding support for more than 40 new connector types to support the most commonly used data systems.<\/p>\n

Our Buyer’s Guide for Data Integration Tools<\/em><\/a> helps you evaluate the best solution for your use case and features profiles of the leading providers, as well as a category overview of the marketplace.<\/div><\/p>\n

As part of its new flex-code data connector foundation, Ascend has also added support for reusable connections, custom no-code user interfaces, and a built-in connection browser to navigate all data available to users. The vendor also offers an extensible framework for connecting to hundreds more data sources than Spark supports natively. Data engineering teams can choose to build their own custom connector types for more specific use cases as well.<\/p>\n

In a media statement about the news, Ascend.io founder and CEO Sean Knapp said “Analytics, machine learning pipelines, and business intelligence dashboards generate the most excitement and attention in today\u2019s data world, but the basic work of data ingestion and unification still adds major delays and complexity to the majority of projects. While many data replication tools do a good job of copying data out of the most common platforms out there, increasingly the most valuable data for businesses doesn\u2019t come in standard formats or from standard platforms.”<\/p>\n

Flex-code data connectors will be available to Ascend Unified Data Engineering customers in December via Ascend Ingest, a suite of capabilities that allows teams to ingest data from any source and in any format.<\/p>\n

Tune in to Ascend.io’s DataAware podcast entitled Unifying Spark Ecosystems with Flex Code Connectors<\/em><\/a> to learn more.<\/p>\n

<\/div><\/p>\n

\t\t\t

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

Ascend.io announced major updates to its Unified Data Engineering Platform with new flex-code data connectors, according to a press release on the company’s website. The Ascend Unified Data Engineering Platform’s flex-code data connectors act as a framework for data ingestion in Apache Spark that bridge connectivity between databases, data lakes, data warehouses, and APIs. The […]<\/p>\n","protected":false},"author":23,"featured_media":3947,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[3],"tags":[853,852,854],"acf":[],"yoast_head":"\nAscend Unified Data Engineering Gets New Flex-Code Data Connectors<\/title>\n<meta name=\"description\" content=\"The Ascend Unified Data Engineering Platform is getting new flex-code data connectors, according to a release on the company's website.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/solutionsreview.com\/data-integration\/ascend-unified-data-engineering-gets-new-flex-code-data-connectors\/\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Tim King\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta 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