Mercedes-Benz USA (MBUSA), headquartered in Montvale, New Jersey, is responsible for the distribution, marketing and customer service for all Mercedes-Benz and Maybach products in the United States. "After thoroughly investigating its options, Mercedes-Benz determined Qlik Gold Client™ (formerly Attunity Gold Client) would best fit their needs, allowing data selection on virtually any parameters developers needed between full refreshes. It would also allow the company to keep costs down by utilizing existing infrastructure rather than purchasing new servers.
Learn how Poly-Wood, LLC, a manufacturer of outdoor furniture, adopted Qlik Compose™ (formerly Attunity Compose) to create a data warehouse quickly and enable access to real-time information.
Tangerine is direct bank in Canada with nearly two million clients and close to $38 billion in assets. They decided to deploy the Microsoft Analytics Platform System (APS), a turnkey Big Data analytics appliance that combines Microsoft SQL Server Parallel Data Warehouse and the Apache Hadoop open-source Big Data platform along with Qlik Replicate™ (formerly Attunity Replicate). Now, they have the ability to turn data into insights simpler and faster, enabling them to be a leader in a competitive marketplace.
Read this case study to learn how the University of Maryland College (UMUC) purchased a Qlik data integration solution through the AWS Marketplace and had the software configured in a matter of hours, replicating data shortly thereafter.
Read this case study to learn how online ticket seller, Veritix, purchased a Qlik data integration solution through the AWS Marketplace and used the software to replicate hundreds of millions of records from Veritix’s transactional databases to Amazon Redshift in just two hours.
Wombat Security Technologies had their data on the Amazon Relational Database Service (RDS) in MySQL, and they needed to move it to a data warehouse on the cloud. Because they regenerate their data warehouse every 15 minutes doing ETL, they needed a solution that could do log-based change data capture.
This solution sheet explains how Qlik delivers solutions that help enterprises modernize their data centers by streaming data from > 40 sources to Apache Kafka® and Confluent in real-time.
Many of the world’s largest enterprises run their critical business operations on SAP applications. Making this data available for business intelligence and analytics is a common need in order to support decisions and analytic processes that improve operations, optimize customer service and enable the company to compete more effectively.
Qlik Replicate™ (formerly Attunity Replicate) for SAP is a high-performance, automated and easy to use data replication solution that is optimized to deliver SAP application data in real-time for Big Data analytics.
Mainframes have been used for over 50 years by large enterprises to manage valuable and sensitive data. But today’s enterprises need to integrate mainframe data into modern, data driven, analytical business processes and the environments that support them.
Qlik Replicate™ (formerly Attunity Replicate) software provides an efficient and cost effective way to bring mainframe data into a modern analytics environment using change data capture (CDC) technology for DB2, VSAM, and IMS. Qlik Replicate provides low-latency and low-impact data integration for mainframe databases. With Qlik Replicate, you can extract mainframe data efficiently in real-time and deliver it to a data warehouse, Hadoop data lakes or Apache Kafka.
For years, data architects and ETL developers have employed a variety of traditional ETL (extract-transform-load) tools as they design, build, manage and update their data warehouses. All too often these tools create manual coding bottlenecks. As a result, the data warehouse takes too long to deliver, cost too much to build and maintain, and cannot keep pace with changing business requirements.
By automating the manually-coded, time-consuming, and error-prone repetitive tasks, Qlik Compose™ (formerly Attunity Compose) will accelerate your DW project, reduce resource requirements and reduce risk.
Understand the challenges that impede the expected return on data lake investments, and discover how to address these challenges to stop pristine data lakes from devolving into useless data swamps.
Learn how to unlock your mainframe data without incurring the complexity and expense that come with sending ongoing queries into the mainframe database; deliver that data in real-time to the most demanding analytics environments; and ensure that your analytics environment includes the broadest possible range of data sources and destinations, while ensuring true enterprise- grade functionality.