Datameer Cloud Data Platform

The Cloud Data Platform

Reliable, scalable, and easy operationalization of data pipelines, so you focus on analytical value rather than focusing on engineering.

Ebook Background

About the Cloud Data Platform Ebook

Organizations are locked in a constant battle to keep up with data demands, especially for analytics. Slow responsiveness and data delivery for analytics to the business limit their decision-making and ability to respond to market conditions and competitive threats. Winning the battle keeps business teams flush with the proper data and facilitates agile business decisions and actions.

Multiple data points show that many organizations are losing this battle or, at best, treading water:

  • A recent Forrester Research study showed that analysts spend 76% of their time doing things other than analysis, including finding data, getting access to data, creating usable datasets, and vetting accuracy and trustworthiness.

  • A Vanson Bourne study revealed that 77% of decision-makers don’t completely trust the data in their organization, and 83% don’t always have access to the data needed to inform timely decision making.

DataOps Process: Drivers and Objectives of DataOps

Analytics Data Management Challenges

Datameer Cloud Data Platform Overview.


Core Platform Services

Data Virtualization. Data Pipelines. Data Catalog. Data Connectors. Compute and Storage.

Analyst Self-Service Tools

Discovery. Modeling. Data Preparation & Transformation. Collaboration. BI and Analytics Tool Integration.

Dataops Tools

Data Engineering. Security. Governance. Automated Operations.


The Datameer Cloud Data Platform is an end-to-end data management platform for analytics that simplifies the data lifecycle. The Datameer platform powers the full data life cycle from discovery, access, transformation, and data governance from disparate data sources to cataloging data assets and provides a framework for sharing results and context among analytic teams.

The platform overcomes all the key challenges to managing data for analytics and eliminating the inefficiencies in the process by:

  • Eliminating analyst’s wasted time spent finding and accessing data through fast discovery and access to data from a robust catalog

  • Dramatically reducing the time to create usable, well-prepared datasets through easy, point-and-click [no coding] modeling and transformation of data

  • Ensuring analytics are built properly by building trust and knowledge around data through collaboration and knowledge-sharing

  • Reducing the time wasted validating proper use of data, and the risks of improper use, through robust security and governance

  • Delivering the scale and efficiency to manage exploding data volumes by virtualizing data as needed or using data pipelines when required to eliminate unnecessary data movement


Discover, Create and Use Analytics Datasets – With the Datameer platform, analysts can find existing datasets, access them, and model their own custom datasets on their own to quickly analyze it and produce fast results.

Create personalized data pipelines – Analysts can use our easy point-and-click tools to quickly create their own ETL++ pipelines or extend existing ones without waiting for IT. Connect, blend with other sources, apply transformation functions, and load into your cloud data warehouse – all without writing one line of code.

Data Engineer & Operationalize ETL++ Data Pipelines – Speed your data engineering processes, improve data quality, and continuously feed your analytics with automated data pipelines. Extract and transform data from any source, blend and enrich it, load it into your CDW or favorite BI tools, then automate the process – all without writing one line of code and keeping data secure and governed.

Transform Data for Data Science – Allow your data scientists to find more datasets to use, discover features faster, and quickly shape them to the needs of their AI/ML engines with single-click encoding functions. Automate these data pipelines to operationalize your AI/ML processes and ensure active governance.

Collaborate with the Entire Team – Use shared workspaces, models, and artifacts for your data engineering and analyst teams to collaborate around data, reuse assets, and govern effectively. Use social-media-like features to share activities and knowledge and robust governance features to promote best practices on where and how to use data.

Build Data Literacy – Share knowledge of data across your entire community of data professionals, analytics teams, and business stakeholders to grow data literacy. Share technical and business metadata, data lineage, business glossaries, data descriptions, and where and how data is used to increase transparency.

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