A data lake solutions is a system or repository of data stored in its natural/raw format, usually object blobs or files. A data lake as a services is usually a single store of all enterprise data including raw copies of source system data and transformed data used for tasks such as reporting, visualization, advanced analytics and machine learning. A data lake can include structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs) and binary data (images, audio, video)
The Difference between Data Lake Solutions and Data Warehouse Solutions
A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose. The two types of data storage are often confused, but are much more different than they are alike. While a data lake solutions works for one company, a data warehouse will be a better fit for another.
Benefits of a Data Lake Solutions
- Ability to derive value from unlimited types of data
- Ability to store all types of structured and unstructured data in a data lake
- More flexibility
- Ability to store raw data
- Democratized access to data via a single
Various sectors in which Data Lake services can be implemented
- Oil and Gas
- Big Government
- Life Sciences
- Cyber security
- Marketing and Customer Data Platforms
Ok, lets discuss with 4 secrets which would make your Data Lake solutions to be amazing
#1. Don’t replace, enterprise data warehouses and data marts
#1. Don’t replace, enterprise data warehouses and data marts
The modern data lake solutions is great for enriching large data sets and correlating data sets that were previously spread across disparate sources. Technologies like Apache Hadoop are ideal for these huge environments because they offer lower costs around storage and processing.
The truth is, you will still find value in your data warehouses for specific types of queries and analytics, so you want to make sure you still retain the best tool
#2. Visual analytics make the big picture accessible
Enterprise data lake services users must be able to get insights without having to code. Otherwise, data lakes are just a private area reserved for technical teams.
To make data analytics as accessible as possible to the larger business analyst community, enterprises must invest in a tool that permits them to visually display that information, ensuring a data lake services isn’t a black box to less tech-savvy users. This feature enables non-techies to drill down into data and derive insights, and even make predictions, through an intuitive interface.
#3. Create a strong data culture
What is the use of having powerful visualize data if it can’t be shared? All businesses employing a data lake solutions need to create a governance framework that enables collaboration.
By creating a framework that allows shareable data sets and dashboards, everyone in the enterprise will be able to offer feedback on which models generate the most valuable insights.
#4. Have unified security and governance
It’s great to have shareable data lake solutions, but it has to stay in the right hands. This topic becomes even more critical as more stringent regulations require more controls in the enterprise.
Businesses must know what kind of data they have, where their sensitive data resides, how to handle it, and how to see it.
Conclusion
While data lake as a services have tremendous potential, they are not silver bullets. Organizations need to set themselves up for success by accompanying their data lakes solutions with apt technologies so they make their data visual, accessible, shareable, secure, and scalable. Hope you enjoyed this read, so that i need a feedback from you people.
Thanks and Regards,
Grace Sophia
Indium Software - Data Lake Solutions | Data Lake As a Services
