Data lakes are not a century-old concept. So, the myth and prejudices about the concept are also abundant. People use it interchangeably with big data and Hadoop.
Big data was in use by the organizations for many decades but was labeled so in 2005 by Roger Magoulas. Gradually Hadoop—a framework to store and process large datasets—led big data towards proper implementation. It paved a road for organized big data management. In late 2010, the founder of Pentaho, John Dixon, coined the term Data Lakes.
A data lake is mainstreaming with big data and Hadoop. Still, what confuses people is the difference between a data warehouse and a data lake.
Let us visit the concepts crisply and understand Data Lake vs Data Warehouse.
What Is A Data Warehouse?
A data warehouse is the foundation of analytics and reporting practices performed over highly cleansed, massaged, consistent, and voluminous data. A data warehouse stores all data coming from the various disparate source systems. These data converge and stay in the data warehouse until archived.
A data warehouse comes into existence after data pass through multiple layers of processing and testing.
ETL—extraction, transformation, and loading—are the processes at the middle layer of an EDW (Enterprise Data Warehouse). Extraction pulls data from the multiple source systems and collates data in a middle layer called staging. Here data transformation is carried out to remove data redundancy, inconsistency, and faults. Transformation substitutes missing data fields with appropriate values too.
After transformation, data get loaded into the final repository called data warehouse fit for analytics and visualization.
What is a Data Lake?
Data lakes are the data repositories that hold raw data not necessarily in the file structure or a hierarchy like the data warehouse. In addition, data in the lake are in the native format. They are not massaged like the highly processed content of the data warehouse.
A data lake uses a flat architecture where each data element has a unique identifier. These uncooked data are extracted for analysis through sophisticated tools only when needed unlike the prepared data in a warehouse.
Being in the raw format, data are inconsistent and unfit to offer insights to the business users directly. However, the data scientists can use the same for information synthesis with the help of mining and profiling tools.
A data lake houses every kind of data piece, be it structured, semi-structured, or unstructured. Structured data are similar to the one in a data warehouse, unstructured are https://logesys.com/backend/images, audio, emails, video, and documents. Semi-structured are JSON files, XML, CSV formats.
Data lakes have been termed many things in the analytics world.
- Repository for self-service analytics
- Raw data reservoir at the enterprise level
- Data management that doesn’t comply with traditional data warehouse
- Synonymous to Hadoop
Nevertheless, a data lake is an enterprise-wide collection of raw data, which can be used for research and analysis by the data experts. It can have all variety of data irrespective of their quality. Building data warehouses out of data lakes is also possible.
Benefits and Usage of a Data Lake
Self-Service Reporting and Analytics Platform
With the sea of structured to unstructured data, analytics experts use data lakes for self-service BI and analytics. If you want to make a report on the native data not present in the data warehouse, a data lake opens a gate for you to enter and wrangle with raw numbers.
As an Analyst, you can just plug in some BI tool capable of data preparation along with the BI capabilities. Then you are set to query the uncooked numbers. This gives a benefit to churn those values and generate ideas and insights that are not directly useful to your users but make perfect sense to you.
Auditing data, date stamps of the figures, and source information that make no valid sense to be present in the data warehouse are available here. Such data make good sense for auditing and version control for analysts.
Sleek Infrastructure, Softwares and Licenses
A data lake comprises data in their original format making them unfit for the use of end-users who lack BI skills. Hence, there is no need for multiple software like expensive ETL and BI tools (limited licenses), and a high-end database. A lake also cuts the requirement of multiple licenses, hardware, and other resources; you can imagine the slash in the expenses.
No large set of security rules apply to a data lake being a concealed layer anyway. Your user base is not going to touch this area of data repository. So, you can open the routes to the data engineers and analysts for self-service tasks.
Performance is another factor. You don’t need to look after the performance, as except a few users, the only heavy load would come from the data massaging and ETL processes. These automated processes are highly organized with speed being a major consideration.
Sandbox for a New Data Mart or a Subject Area
Need to add a new subject area into your data warehouse? No need to check the feasibility until the last point of EDW. Just pull data from the new source system into the data lake. Run profiling and massaging processes, and check if it works well up to this level.
A data lake acts as a sandbox for new subject areas that demand a high volume of maintenance up in the system.
What if the new subject area were for short-term analysis after which it needs to a decommissioning? A data lake is the right place to accumulate data of this variety and run data tests using web-based tools like Power BI.
Not just self-service BI, a data lake can be helpful in predictive analytics by using web-based tools like Qlik and Power BI.
Data Lake and Big Data
Data lakes are the answer to big data management issues. Owing to the large volume and unstructured data, big data has scaling challenges, which a data lake can handle. These can accommodate data from various departments overcoming the silos problem. Lakes help to have a holistic approach for the organization level data that are available in the haphazard format.
The investment with a data warehouse for such scenarios would be a whooping panic to the finances.
Moreover, data lakes can integrate AI and IoT technologies for advanced analytics and insights generation. Lakes open the floodgate for new opportunities for your organization with improved decisions.
Whether you have structured data, unstructured, or a mixed bag, generating the right insights is more important than the technology and methods. Deciding to use a data warehouse, a mart, or a lake depends on the input available and output to be produced.
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