, (NASDAQ: QLIK), a leader in user-driven Business Intelligence (BI), and
, provider of the
Real-time Database for Big Data Analytics
, today announced at Qonnections 2014, Qlik’s annual Global Partner summit, that ParStream’s on-demand data analysis capability has been integrated with the in-memory data analysis functionality of QlikView to help
gain more detailed and granular actionable intelligence from transactional sales data.
MPREIS is one of Austria’s largest retail chains with 228 supermarkets and 160 bistros. The company had been using QlikView, the market-leading data discovery solution, for over three years for sales and business analytics for all of MPREIS’ sites.
“To maximize the value MPREIS got from its sales transaction data, they needed a flexible, big data solution that allowed them to easily search and query in-real time,” said Les Bonney, Chief Operating Officer, Qlik. “The integrated solution from ParStream and Qlik provides the on-demand analytics they require along with the in-memory and big data capabilities of QlikView Direct Discovery. As a result, MPREIS has increased its data import capabilities and gained a powerful and comprehensive solution that can manage high performance and real-time analytics seamlessly.”
“Before this work, MPREIS reached a barrier when analyzing 500 million rows of data. Now, the combination of Qlik’s in-memory engine and ParStream’s Real-time Database for Big Data Analytics empowers MPREIS to analyze six billion rows of data in as little as 450 milliseconds,” said Michael Hummel, CEO of ParStream. “This allows MPREIS to go beyond pulling general sales numbers to being able to pull detailed, granular analysis such as when, where, and even to whom specific products were sold. This truly has revolutionized their business.”
Being able to perform well in varying situations, the Qlik and ParStream solutions are highly complementary technologies for organizations of any industry with big data strategies. Businesses can load their aggregated data on a yearly and monthly basis into QlikView and can continue to use their dashboards and applications in the same way as they did in the past. Furthermore, enterprises can load daily and real-time data into ParStream. The ability to analyze historical data simultaneously with newly imported data – what ParStream calls
– is vital for a business’ longevity and profitability by helping users make informed decisions based on real-time information.
To discover how the QlikView Business Discovery solution and ParStream’s Real-time Database for Big Data Analytics can help retailers – as well as businesses in other industries – maximize the value of their big data strategies, visit
ParStream helps businesses gain immediate insights into their data. As the leading provider of real-time databases, ParStream powers big data analytics on the massive amounts of data businesses collect daily. The award-winning, breakthrough technology enables ultra-fast, flexible analytics of stored and streaming data. The market-agnostic ParStream Real-time Database is used worldwide in e-commerce, telecommunications, finance, advertising, retail, mining, and science. Based in Cupertino, Calif. and headquartered in Cologne, Germany, ParStream has been awarded
“#1 Big Data Startup”
by CIO Magazine, recognized as a “Cool Vendor” in the Advancing Data Management Maturity report by Gartner, and was named
“Trend-setting Products in Data of 2014”
by Database Trends and Applications.
For more information, visit
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Qlik (NASDAQ: QLIK) simplifies how people explore their data to help them make better decisions. With its QlikView Business Discovery platform people quickly bring data sources together to create dynamic visual applications that can be navigated and searched intuitively. QlikView uses Natural Analytics™ to reflect the way human curiosity searches and processes information, while delivering the enterprise manageability, governance and service offerings organizations require. Qlik and its global partner network support approximately 31,000 customers in over 100 countries.