Data Lake Solutions

ExistBI is ready to leverage our decades of implementation experience to help you successfully implement your Data Lake solution and create a modern data warehouse that follows industry best practices.

What are Data Lakes?

Data Lakes are used to reliably store structurally and spatially heterogeneous data sources with complex storage modes. These data sources are designed to be accessible at any time to help support your business decisions.

Synonymous with a modern data warehouse, Data Lakes came about as users faced larger and more complex challenges set by new innovations and the progress of technology, which in turn imposed new demands on data storage systems.

Data Lake architecture enables the retrieval, processing and instant transformation of data into valuable information from a single source, on demand.

What are Data Lakes?

Big Data and Data Lakes

This shift in “Big Data” has resulted in a new and conceptually different approaches to data storage – storing all types of data in a single location regardless of size and complexity, using increased computing power with massive parallelisation and distributed processing. This approach provides customers the ability to process large amounts of data in a negligible amount time and with minimal load to current systems.

While the standard data warehouse model traditionally stores data in a hierarchical structure, Data Lake architecture assigns each data element a unique identifier that contains extended metadata tags associated with the corresponding element.

When required by business operating procedures, analysis can be performed at any time on relevant data groups stored within a data centre of the Data Lake. The ability to analyze the data so readily, transforms data into powerful and applicable user information.

Big Data and Data Lakes

Let us help you with your Data Lakes project

Let our data consultants help you with your data warehousing or data lake project.

Data Lakes vs Data Warehouses

Data warehouses are information bases for traditional company reports or audit assessments in medium and large companies. Structured data collected days, weeks or even months ago is often prepared and analyzed in an ETL (Extract, Transfer and Load) process.

Data lakes are scalable, can act as a kind of cache for data warehouses, and are a low-cost way to store files in any format. This is particularly attractive for less structured data such as documents, images, emails and audio files.

Both have their advantages and disadvantages and which one to choose is dependent on your application.

What Is The Difference Between Data Lake And Data Warehouse?

Data Lakes vs Data Warehouses

Data Lakes Case Studies

Manufacturing Industry
Finance and Banking Industry
Education Sector

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