I use Datameer for building use-case-driven data pipelines. We are also now using it to ETL on-premises data to a cloud data warehouse. These pipelines are used to enable reports and dashboards for various business units in my organization. Using Datameer has saved us a lot of time and money and we didn't need to hire a data ops team. Datameer is easy to use with no need to write code, so business teams are even using it.
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Several departments are using Datameer to aggregate large amounts of data from disparate sources.
Datameer is being used as a proof of concept for Hadoop by information technology and engineering. We are trying to provide an analytical platform which can potentially be used across all the departments in the organization. We don't have a lack of data, and are focusing on use cases where unstructured or semi-structured data is relevant. For example, text notes used Customer Service to document cases and follow-up internally with other departments.
Datameer is being used by our my organization as a Big Data BI tool on top of our Hadoop Cluster along with Hive, Impala, Pig, and HBase. It helps non-technical analysts to gain the power of running high power analytics on large data sets. It simplifies the SQL or scripting languages used in the Apache/Cloudera tools into a user GUI which supports almost 200 built-in functions and holds endless possibilities with the support of custom functions. Datameer also has the ability to import PMML models from SAS and other tools and create custom functions from these imports. Datameer is used across the whole organization and is growing on many analyst as the next up and coming analytical tool.
Datameer enables data engineers and analysts to transform and model data directly in their cloud warehouses via a simple SQL code or no-code interface to solve complex analytical projects.
- Supported: Connect to traditional data sources
- Supported: Connecto to Big Data and NoSQL
- Supported: Simple transformations
- Supported: Complex transformations
- Supported: Data model creation
- Supported: Metadata management
- Supported: Business rules and workflow
- Supported: Collaboration
- Supported: Integration with data quality tools
- Supported: Integration with MDM tools
- Amazon Redshift
- Google BigQuery
- Azure Synapse Analytics (Azure SQL Data Warehouse)
- VMware Tanzu Greenplum (Pivotal Greenplum)
- SAP IQ (formerly SAP Sybase IQ)
- IBM Netezza Performance Server
- Azure Blob Storage
- Amazon S3 (Simple Storage Service)
- Google Cloud Storage
- Microsoft SQL Server
- Azure Cosmos DB
- Azure Data Lake Storage
- Microsoft Power BI
- Tableau Server
- Tableau Desktop
- Tableau Online
- Amazon Athena
- Qlik Analytics Platform
- IBM DB2