Data-driven decision making has transformed how businesses operate. Corporations are leveraging data to identify patterns and enable decision making. Corporations operating in industries ranging from travel, automotive, advertising to telecom have derived business benefits through data analytics.

The demand for real-time analytics in conjunction with Artificial Intelligence, Machine Learning, and data visualization has been expanding exponentially. This creates a demand for a platform that can store a large volume and variety of data.

A data lake can be thought of as that platform. Though without a mechanism to maintain the data lake in a proper format, it might soon turn into a dumping ground of data without any mechanism to analyze it; as highlighted in the article “Beware the Data Lake Fallacy” by Gartner.

Data lake ecosystem

One of the platforms that have been playing a centerstage role in the Data lake ecosystem has been Hadoop. With its capability to store a large amount of data while at the same time deploying its significant processing power, it is perfect for use cases that address the challenges that big data presents.

Hadoop can be seamlessly deployed to address scenarios around analytics, machine learning, and data mining.

The popularity of Hadoop can be gauzed from the fact that nearly every large cloud provider is now offering a Hadoop instance on its platform and building capabilities similar to it. Azure data lake storage, Snowflake, and Redshift now offer almost similar capabilities as HDFS.

Data lakes architectural model emphasizes data acquisition solution with a principle to “load first and ask later”. This strategy has implications over various aspects ranging from governance to the generation of disparate data copies, and lost control, says Woods at Forbes.

Data Lake often winds up as repositories of raw data that gets stale over some time. With multiple copies of data that are ungoverned, Data lake can easily lose its relevance. Though modern data lake offers unparalleled processing power. It also gives rise to complexities in operations which makes Data clusters difficult to deal with.

Each passing day pours more and more data into the lake and hereby adds up to the concerning situation. A combined approach with data virtualization eliminates these critical challenges, enabling analysts to harness the full power and value of Big Data.

Data Virtualization

This is where Data virtualization comes into the picture. This can be the answer to the complexities emerging from a centralized repository. So what exactly is data virtualization? In simple terms, Data virtualization provides all the features of a data lake with the exception of not requiring the need to physically replicate data. Because of this capability Data virtualization can integrate various types and sources of data across locations.

Data Lake with Data Virtualization

The idea to create a logical data layer unifying data for centralized governance and security was first taken up by Mark Beyer. Data Virtualization offers real-time data delivery in business environments, integrating enterprise data across the disparate systems and format into a single, virtual view.

The logical approach to data management enables complex models to access data from varied systems and keeps track of the data lineage and transformations. The virtual layer centers itself around the physical data lake and leverages its storage and processing capabilities to its full potency.

Virtual Data Access

Data Virtualization enables access to data from its source location without necessarily loading it to the data lake. At the same time, it also caters to data loading, data cleansing and transformations for optimal performance as per the requirement.

Data virtualization can also make use of metadata to make sense of each data irrespective of its location and format. Bridging the gap between disparate data sources and data silos, it enables a totality view of data while avoiding copies and speeding up delivery.

Data Management

Raw data aggregates from higher granularity applications and data stores can be logically curated, facilitating only desired data to be brought into the system. Data virtualization also enables data streaming to the physical data lake for devices and systems not holding a storage space.

Data abstracts are captured via a simple to use SQL engine interacting the back end complexities. Analysts, as well as non-technical users, can leverage data catalogs without extensive repository search.

Agility and Data governance

Opposite to time-consuming persisted approach to data changes. Data Virtualization swiftly handles new data from its source connection keeping track of data lineage and preview. The logical approach ensures data to be consistent, accountable and secure.

With a single layer view of trusted data, organizations can also avoid non-compliance costs on grounds of data regulation and violation.

In Conclusion

Data Lake complemented with a virtual layer addresses various challenges and complex scenarios faced in big data architecture. Making potent use of big data tools increases profitability and implicates cost-savings at the enterprise level. Data virtualization lowers infrastructural costs and streamlines data, reducing decision-making time-efficient for the business. While at the employee level, the approach facilitates productivity with more time for running analytics, improving product experience and hereby opens new doors of opportunities for more revenue and value generation.