An Overview of Cloud Data Warehouses and Cloud Data Lakes

The IT environment is constantly changing, but something that has stayed consistent over the years is the need for organizations to perform accurate analysis, create reports and derive results to make critical business decisions. In this article, we will review how companies are achieving this through cloud data warehouses and cloud data lakes.

Data analytics has expanded beyond business intelligence reporting, and the demand for advanced analytics on a variety of new data sources has increased to reveal deeper insights for predictive modeling, data mining etc. This gave rise to the concept of data lakes. With the evolution of cloud computing and data warehouses migrating to the cloud, data lake creation/deployments to the cloud support easy integration that help users produce proactive business actions.

Let’s look at some of the key features of both a cloud data warehouse and cloud data lake.

Cloud Data Warehouse

Smartbridge is currently working with clients to help migrate their legacy data warehouse to the cloud and modernize their data management architecture using Azure SQL Data Warehouse and Snowflake.

Cloud data warehouse architecture uses massively parallel processing (MPP) with unlimited storage and computing power that can be scaled in, scaled out, and paused depending on the demand. It provides a single source of truth that helps making business driven decisions easier. Here are some cloud data warehouse benefits:

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  • Data warehouses are consolidated and widely used for key operational business reporting

  • Highly modeled and used to solve specific set of current problems like using data to explain the answers, check the trends, and share insights across the organization

  • Data elements are well defined and transformed into the right structure to derive required results hence follows a ‘schema-on-write’ approach

  • Data is well governed, secured, and easily understood by business users that requested it

Cloud Data Lake

A cloud data lake is a repository of data in raw form, allowing multiple potential uses of data from a single load. It contains all types of data from unstructured machine generated IoT data, data from human interactions through emails, twitter feeds, videos, audios, semi structured data like JSON, XML, in addition to the structured data. Modern data lakes, when managed right, work as a great platform to easily store, load, integrate, and analyze data.

Key technology players like Microsoft and Amazon offer data lake platforms by utilizing their existing storage services.  Azure Data Lake Storage (ADLS) Gen2 uses Azure Blob storage and ADLS Gen1 to provide a platform combining rich features of low cost storage tiers from the Blob storage and hierarchical file system (HFS) from the ADLS Gen1. Similarly, AWS uses the Amazon S3 storage along with some of its other services to define its data lake architecture.

Why consider cloud data lake?

  • Data Lake architecture supports storage of denormalized tables or files enabling users to carry out their self-service analytics on the same data source.

  • Optimized to process high velocity, volume data with minimal latency which fits streaming data inputs.

  • Opens access to diverse data sources previously unavailable to the business.

  • Extensively used by data scientists to enable exploratory data analysis.

  • Used for discovery purposes, data lakes make it easier to run complex machine learning applications/algorithms.

  • Can be used as a landing area before the data is consumed by the data warehouse for the current reporting needs.

  • Preserves the native form of data thereby storing history and enables addition of newer data.

  • Follows a ‘schema-on-read’ approach where the data can be ingested for several downstream purposes and stored until business need/value of data is determined.

  • Supported by ELT (Extract, Load and Transform) strategy.

Having looked at the features we can conclude that data warehouses and data lakes are both data repositories that store and handle data for various business needs, but serve different purposes. They are usually complementary to each other and are utilized depending on the needs of the organization. For instance, visualization tools like Power BI can also connect to ADLS for producing visualizations from exploratory files in the storage account, like the way Power BI connects to the data warehouse for producing dynamic reports to visualize data. As a result, many organizations are switching to use a hybrid architecture to tap the value of their data ecosystem end to end.

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