There’s no denying that Cloud has evolved from being an outlying market disruptor to a mainstream method for delivering IT applications and services. In fact, it’s not uncommon to find that Enterprises use the services of more than one cloud at the same time. However, while a multi-cloud strategy offers many benefits, it also increases data management complexity and consequently reduces data availability. This webinar defines the meaning of DataOps and why it’s a crucial component for every multi-cloud approach.
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The Importance of DataOps in a Multi-Cloud World
1. A P R I L 4 , 2 0 1 9
KEVIN PETRIE
SR DIRECTOR, ATTUNITY
DATAOPS FOR MULTI-
CLOUD STRATEGIES
DATAVERSITY WEBINAR
DataOps is a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization. The goal of DataOps is to deliver value faster by creating predictable delivery and change management of data, data models and related artifacts. DataOps uses technology to automate the design, deployment and management of data delivery with the appropriate levels of governance and metadata to improve the use and value of data in a dynamic environment.
Market evolution aligns with our uniqueness
The Attunity solution generates real time data streams, at scale and with low impact, from heterogeneous sources that include production databases, data warehouses, SAP and mainframe systems, as well as flat files and SaaS applications. We deliver data to all major target platforms, then refine it for analytics.
Our solution enables three data delivery options.
In the first option, we replicate committed transactional data, either in bulk or via real-time change data capture (CDC), to relational databases, ranging from Oracle to SQL Server to PostgreSQL, on premises or in the cloud. We also can replicate to or from flat files. This enables use cases such as migrations, reporting and analytics.
The second scenario supports data warehouse modernization initiatives. We deliver the transactional data streams to modern data warehouses such as Snowflake and Azure SQL DW, using automated data delivery and modelling methods to enable your reporting and analytics activities. We automate model creation, data warehouse creation, data mart creation, table creation, data instantiation and source/target mappings. You can manage all these tasks as an integrated workflow.
Third, we take relational transaction streams and stitch them together into a common format that can be readily consumed for analytics on Big Data platforms such as Amazon EMR, Azure HDInsight and Google Dataproc. Our solution automatically creates, loads and updates data stores, and accelerates dataset readiness for analytics. You can then process the datasets we refine alongside non-relational and unstructured data from other sources.
Because all these capabilities are native to the Attunity data integration solution, you are able to prepare data for analytics with fewer software components.
[Internal note: we support structured and relational data. We do not support unstructured and/or non-relational data.]