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To stay competitive in the data world today, every organization needs to become data-driven. As a result, data-driven companies are said to have a faster decision-making process by54 percent, able to make effective communication with … Read More
To stay competitive in the data world today, every organization needs to become data-driven.
As a result, data-driven companies are said to have a faster decision-making process by54 percent, able to make effective communication with stakeholders by 54 percent, team collaboration by 51 percent, and making business agile by 46 percent.
However, a non-data-driven organization is seen to be slow at grasping the significance of data with nearly 39 percent finds it critical for their organization. This clearly shows that there’s still a gap which is keeping business disconnected from the use of data.
Below are a few trends in a data infrastructure that is likely to rule in 2021.
Data lakes, a storage repository wherein you store big data gathered from multivariate sources which are still raw and granular. Structured, unstructured, or semi-structured data can be stored easily and used for future purposes.
Most often people get confused between terms like data lake and a data warehouse. The difference here is, a data warehouse is used by analysts typically to collect business intelligence. These consist of flat files that are used for their work. Whereas with a data lake, these are being set up with the help of data engineers. As a result, you will find most data scientists being busy working with data lakes since they have a wider scope.
To simply understand, the definition of data lakehouses is as simple as the word itself. It is a combination of both data warehouses and data lakes.
Certain platforms like Databrick’s Delta Lake and Snowflake managed to create a solution to have one source for data which has both benefits of data lakes and snowflake.
In short, these data lakehouses help in implementing data warehouse management features and data structures. This helps cut down costs of storing data. It also helps retention of schema and creating a new version of data. As a result, it helps data scientists perform both business intelligence and machine learning.
Here’s what George Fraser, CEO of Fivetran says, “I think 2021 will reveal the need for data lakes in the modern data stack is shrinking.” Adding that “…there are no longer new technical reasons for adopting data lakes because data warehouses that separate compute from storage have emerged. In the world of the modern data stack, data lakes are not the optimal solution. They are becoming legacy technology.”
The term data orchestration is still a new concept that helps visualize data, abstract data access from various storage systems, and present data. And data orchestration platforms help organizations become data-driven. How? By simply combining isolated data gathered from multiple sources and by making them usable. Perfect, Apache Airflow, Stitch, and Luigi are certain examples. These are easily compatible with modern software approaches like continuous integration, DevOps, and version control.
With data growing at breakneck speed, companies have started making huge investments in their teams to help find the data required, document, and to reduce their workload. Lyft Amundsen and Uber Databook are two great examples of data discovery engines used for improving the productivity of the data users. These engines provide a search interface for data. Now such tools greatly rely on metadata, this further gives aid to productivity and compliance. And as infrastructure accelerates so does the company growth. The major goal of data discovery tools is to make data reasonable – find data, make it accessible, exchangeable, and that can be used.
The big data world is changing significantly thus opening doors for technological transformation. More so, a big data professional now has a much more critical role to play in organizations.
The aforementioned trends in data infrastructure can serve as a catalyst to organizations that are still struggling to become data-driven.