- Why DI?
CloverETL Designer is a visual tool for development, debugging and manual execution of data transformations
is an enterprise-grade runtime platform for automation and orchestration of data integration processes
Scale up to multi-node architecture for parallel data processing and to assure high availability and geographic “close to data” distribution
Our free open source ETL version of CloverETL Designer. Visually develop, debug and manually execute data transformations.
Let our high performance consulting operation find solutions to your business challenges.
New team members? New project? Freshly upgraded? Take training and increase your CloverETL working knowledge by both breadth & depth.
Discover corporate, individual and special offers for Expert Services.
Discover CloverETL’s flexibility. Check out some use case examples.
On demand videos and documentation designed to help you get started with CloverETL.
A distillation of the most common questions.
On demand video and documentation designed to help you get started with CloverETL.
- Quick start
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The Buyers’ Guide is designed to help you select the correct data integration software and vendor for your organization. The guide will walk you through the decision-making process, step by step. You’ll learn what to consider and what questions to ask before committing to a data integration solution.
What is a data migration—really? How involved do you need to be in the process? Is it just an IT problem, or does the whole business have to devote attention to it?
AnaCredit (The Analytical Credit and Credit Risk Dataset), is a project from the European Central Bank (ECB) to create a shared database containing information on bank loans to companies. Starting from September 2018, data collection for AnaCredit is due to start, and the regulation will come into full force in 2020. The e-book answers 10 key questions, ranging from how data collection will work, to how firms should prepare for the regulation.
The Guide to Data Migration Projects breaks down a typical data migration into 13 project stages. It explores best practices and some red flags so that you can take the right approach to delivering your project.
To enable a global logistics company to have a single, unified view of their data, the company turned to CloverETL for a solution which would leverage and bring together their shipment data.
CloverETL enables Customology to gather and unify marketing and transactional data to deliver customer‑centric campaigns backed by data science. With CloverETL, Customology has shortened the time to market by replacing bespoke application development with a robust and flexible data integration platform.
To successfully deliver a complex data migration within a tight two-month schedule, a multinational software company turned to CloverETL for an automated data mapping framework.
Introducing a data ingestion and validation framework based on CloverETL has radically changed how quickly consultants from a Workday Implementation Partner can move past the migration stage of a project, ultimately leading to more valuable time spent properly implementing Workday.
To replace expensive manual data processing of monthly phone call statements, a company implemented an automated data integration solution.
With poor quality customer data, a publishing house was having trouble successfully executing their direct marketing campaigns.
For a fast-growing logistics company, new territories means more address data of varying structure, particularly difficult to validate. To replace a hard-to-scale data bottleneck of 30+ workers, the company invested in an automated solution built on CloverETL.
A major financial and leasing provider successfully deployed a CloverETL data migration solution to cleanse and consolidate AS/400 and Dynamics NAV legacy systems to ERP, increasing processing speed by one-third and significantly reducing annual operating costs.
To make more informed marketing decisions regarding their subscriber base, a major international satellite TV service provider needed to consolidate their data and implement a data warehouse.
Data integration is a powerful tool, but can often be beyond the technical level of business users. Data models can be easier for less technical teams to work with, but they only tell part of the story. This white paper, featuring a financial services case study, looks at how these two worlds can be bridged, with a technique that enables business and technical teams to establish a common language.
Bad data is unavoidable, and even small data issues can have enormous impact. This whitepaper examines how businesses can architect systems from the ground up to better manage bad data, and which tools and practices can be utilized for a more effective data validation and correction loop.
The volume of data is increasing by 40% per year (Source: IDC). In addition, the structure and quality of data differs vastly with a growing number of data sources. More agile ways of working with data are required. This whitepaper discusses the vast options available for managing and storing data using data architectures, and offers use cases for each architecture. Furthermore, the whitepaper explores the benefits, drawbacks and challenges of each data architecture and commonly used practices for building these architectures.
With a well-designed data anonymization process, it's possible for businesses to obtain reliable test data that provides the same use case coverage as the original production data—without falling victim to data security, privacy, and licensing issues. This white paper discusses reasons for and best practices of data anonymization and the advantages of a maintainable, customizable approach with CloverETL.
Utilizing the power of a data integration tool, organizations can future-proof towards large data volumes and complexity. This white paper discusses how to make the most of the data integration layer when designing data applications.
It's no secret that data assets are increasing exponentially. With this dramatic growth in volume and complexity, the need to move, manipulate, and analyze data is taking center stage. Today's imperative is to design a data workflow optimized for performance, agility, and usability.
New rapid data integration techniques are available to cost-effectively reduce the intensively manual "human integration".
What exactly is the Big Data buzz about? Is it possible to work with big data without "Big Data"?