- May 28, 2020
- Posted by: Admin
- Category: Data Management, Technology
Agility and real-time insights have quickly become the keystone of the data-driven enterprise. And for good reason, according to Gartner, by 2022 companies will be valued on their information portfolios.
It goes without saying that being able to move data at the speed of change makes the difference between a successful business and one that falls short.
As organizations continue their digital transformation journeys, adopting modern technologies such as the cloud and data lakes, it’s important they also embrace a modern approach to data integration.
In 2019, IDC predicted the sum of the world’s data will grow to 175 zettabytes in 2025—a 61% compounded annual growth rate. Considering this exponential growth of data, for many businesses modern data integration may seem too steep a mountain to climb—but in reality, the climb isn’t as tough as it may appear.
By collecting and interpreting multiple datasets, modern data integration eliminates information silos, democratizing data access, and providing a consistent view to users. This in turn helps create agile, integrated data environments that enable companies to respond faster to change, better leverage new technologies, and develop innovative products and services.
Enter DataOps
Modern data integration has become a catalyst for a new breed of data operations (DataOps). DataOps is an emerging set of practices and technologies for building data and analytics pipelines to meet business needs quickly. As these pipelines become more complex and development teams grow in size, organizations need better collaboration and development processes to govern the flow of data from one step of the data lifecycle to the next—for example, from data ingestion and transformation to analysis and reporting.
DataOps was born from the need for a new methodology that would encompass the adoption of modern technologies and the teams using the data. It leverages real-time integration technologies, such as change data capture (CDC) and streaming data pipelines, to ensure data is readily available for use, typically in a self-service model or integrated with current systems.
As a result, DataOps streamlines how data owners, database administrators, data engineers, and data consumers interact, as they all use data to improve decision-making and achieve business goals.
Bringing IT and the business together
DataOps allows organizations to gain insights—and ultimately, take action—quicker than before by continuously processing new data, monitoring performance, and producing real-time insights. However, users should have full visibility of the data they’re using, including when, where, and by whom it has been modified, making data catalogs the backbone of DataOps.
After data has been created, extracted, transformed, and integrated, a data catalog informs users of available datasets and metadata on a specific topic, providing assistance in locating data required to build analytics. Essentially, a data catalog is an inventory of data that enables users to glean accurate and trustworthy critical business insights. It holds information on the datasets, offering quality assessment scores for critical factors relevant to users—for example, whether the data is clean if it’s being used by other teams, and so on.