Make real-time decisions through big data and traditional data convergence using data virtualization
Big Data Is Here to Stay
All businesses now have one thing in common – they all are data businesses. Every organization is trying to make use of their big data and streaming data by turning those into information and knowledge to fuel business growth.
Volume, variety and velocity of data continues its upward movement and seems to stay the same for the foreseeable future, based on proliferation of internet connected devices, web platforms and trends such as cognitive science, machine learning and IoT. Adding to the complexity, IT wants to empower all big data scientists and business users through self-serviceable analytical and reporting platforms.
But You Need More Than Just Big Data
While big data offers a lot of promises to fuel business growth across industries, it comes with a set of challenges that are consistent across all shapes and sizes of organizations.
- Big data is no more synonymous only with Hadoop. Spark, Hive, Presto, Kafka, Impala is crowding the big data and streaming data storage and query space. Heterogeneity brings information inconsistency among various business units within any organization, as each user group have their own wish list from big data analytics.
- Data privacy and data security concerns are more pronounced and prominent with the rise of big data. More silos of big data means more separate data privacy and data security requirement per silo.
- There is a huge proliferation of consuming applications over the years and a lot of them do not interact very well or at all with various sources of big data or streaming data.
Extracting Value of Big Data with Data Virtualization
Data virtualization technology provides an agile and cost-effective approach to combining, governing, and managing big data, and to overcoming the inherent challenges presented by big data silos. We call it big data virtualization. There are three most popular use cases of big data virtualization.
LOGICAL DATA LAKE
Data virtualization bridges one or more data lakes along with traditional data warehouses, MDM systems, cloud sources and beyond. This use case improves enterprise functionality of data lakes by providing additional context with data from other enterprise sources.
DATA WAREHOUSE OFFLOADING
Data virtualization offloads less frequently used or cold data from enterprise data warehouse to a Hadoop cluster to free up expensive enterprise computing resources.
Data virtualization combines streaming data with other sources of enterprise data to make streaming data more meaningful and useful for business users.
Benefits of Using Big Data with Data Virtualization
Virtualizing and combining big data along with other sources of enterprise or cloud data offers many benefits, so that organizations can truly reap the benefits of big data:
REDUCES EXPENSIVE BIG DATA REPLICATION
across the organization, at the same time offering significantly faster time-to-market.
CREATES CONSISTENT DATA GOVERNANCE
privacy and security structure across wide range of systems, both on-premise and cloud.
OFFERS FLEXIBILITY AND AGILITY
to big data and IoT analytics by offering easy connectivity across a broad range of source and consumer systems.
SIMPLIFIES INFORMATION CREATION AND CONSUMPTION
model by creating an abstraction layer so that business users are separated from underlying complexity.