Daily Archives: December 6, 2017

Differences Between Data Lakes and Datawarehouse

The main reason for writing this article is to project the difference between data lakes and data warehouses for helping you to know more about data management. Most of the data and analytics practitioners will understand the term. Let us see the main differences:

  • Data Lakes Retain All Data

While developing the data warehouse there is a need to invest a good time to analyze data sources and understand the business processes and profiling data. You will get a highly structured data model, especially for reporting. In this process, the major work is to identify the data to include and avoid. The main thing over here is to make decisions about the type of data to add and to reject in the warehouse. Normally in a report that is defined in the data is not referred for answering particular questions, it will be deleted from the warehouse. For simplifying the data model this is particularly done and also for protecting space on costly disk storage that is used for making the data warehouse performant.

  • Data Lakes Assists All Data Types

Normally the data warehouses consist of data taken from the transactional systems and are composed of quantitative metrics and they are defined by the attributes. Sensor data, web server logs, social network activity, text, and images are avoided and they are termed as Non-traditional data sources. It will quite difficult and expensive for consuming and storing the data. The non-traditional data types are approached by the data lake irrespective of source and structure in the data lake. Schema on reading vs the Schema on Write is the approach used in the data warehouse.

  • Data Lakes Support All Users

Here you can find 80% or lots of users are working. They want to obtain reports and check their performance metrics or slice in a spreadsheet daily. For these users, the data warehouse is actually ideal and it is quite structured and easy to use and understand and for answering these question it is built with some object.

The data is analyzed more on the next 10 percent. The source used over here is the data warehouse but often revert back to source systems to obtain the data that is not added to the warehouse and sometimes get the data from the external organization. Their new reports created are spread everywhere in the organization.

  • Data Lakes Adapt Easily to Modification

The important drawback of the data warehouse is its longer time consumptions for changing them. While developing there is a lot of time invested and obtain the warehouse’ structure correctly. It is a familiar fact that a good warehouse will be submissive to change but it will take a lot of time for the loading process and the work was done to make analysis and report easy.

For the data warehouse team, there are lots of business questions for adapting their system to respond them. The concept of self-service business intelligence is done by rapid answers. Since the entire data is present in its raw form and can be managed by someone else who needs it and the data can be explored by the users to go ahead of the structure of the warehouse in the novel ways and respond their queries.

  • Data Lakes Provide Rapid Insights

This difference has been got from the other four points and the reason is that data lakes contain various data and data types as it enables users to fetch their results on a rapid way when compared to the traditional data warehouse approach. Moreover, this early access to data arrives at a price. The data warehouse development team does the work and will not do work for some or other data sources needed for an analysis. There are lots of structured views of the data in the data lake that actually looks like what they have had earlier in the data warehouse.

Join DBA Course to learn more about Database and Analytics Tools.

Stay connected to CRB Tech for more technical optimization and other updates and information.

Don't be shellfish...Digg thisBuffer this pageEmail this to someoneShare on FacebookShare on Google+Pin on PinterestShare on StumbleUponShare on LinkedInTweet about this on TwitterPrint this pageShare on RedditShare on Tumblr