The CloverETL data integration suite includes CloverETL Designer, used to design data movement from and to various sources and targets; and CloverETL Server and Cluster, for automated, highly scaled data runtime. Organizations such as Moody’s, Thomson Reuters, SHOP.CA, IBM, and Oracle employ CloverETL as a core part of their data integration operations.
CloverETL emphasizes that the definition of “rapid” in rapid data integration means more than just processing power; it also refers to an end-to-end process that begins the moment a data-related problem is recognized to the point when it’s solved. CloverETL president and co-founder David Pavlis said, “Rapid is the combination of how quickly a company can begin Integrating; how intuitive a software suite is to use; and what tools it gives you to ensure accurate data.”
What sets CloverETL apart as a company is its depth of expertise and focus. “For a software company, at the end of the day, great code solves real problems. We work to avoid distractions, buzzwords, and hype,” said Pavlis. “Our goal is to solve tough issues in data integration, which in turn helps customers execute better data warehouses and analytic applications. Everything starts with data integration, and customers recognize that it’s the foundation of their enterprise.
This focus on data integration also means CloverETL can solve customer problems more effectively. “We do the heavy lifting of data integration. Our focused approach means we free up our clients’ precious resources, so they can invest in other IT areas.
CloverETL is a rapid, end-to-end data integration solution known for its usability and intuitive controls, along with its lightweight footprint, flexibility, and processing speed
To that end, CloverETL’s roadmap includes new ways to move types of data more rapidly and efficiently. The first is a feature called subgraphs. With subgraphs, developers can wrap functional blocks of a data transformation into new high-level components that are well documented and easily reusable. These can then be shared with other users to design complex transformations with an easy top-to-bottom approach. Subgraphs also boost knowledge sharing and team productivity. Because each subgraph captures expert knowledge in a single component, team members with differing levels of expertise can use it.
Another CloverETL investment includes data agents, addressing the quickly evolving “Internet of Things,” or the exploding number of connected machines and devices generating enormous amounts of data. Pavlis said, “We work with a company that manages cell phone tower networks, each of which has a computing device that produces tremendous amounts of data. We can use data agents which, in essence, are scaled down versions of CloverETL on each device to extract the data, transform it into a useable format, and load it into a destination where it can then be analyzed.
Pavlis sees a bright future for CloverETL’s focus on data integration. With each challenge, CloverETL rapid data integration continues to grow along with the market. According to Pavlis, “Business leaders are demanding more from their organization’s data as it creates new revenue and reduces costs. For us, that means there are always more data integration problems to be solved.”