Why Data Lake?
Why data lake is used? because it is used for storehouse of data. Data lake is a system or storehouse or repository of the data stream with their original form. It can allow the user to evaluate and analyze the data.
- Each and every day, we produce more amounts of data throughout the world. Most of the data produce in the unstructured form of data. Storage of data is now being a big part and analyzing the data.
- Data lake is a storehouse of collection of raw data format into the structured data, unstructured data and semi – structured data format. Now, Data lakes are widely used in many companies and organizations for the analytic of data, machine learning techniques, operations for data and real time data.
- Some of the benefits of data lake are,
a) Normalizing, scalable and versatile of the data.
b) Improving data quality.
c) design elasticity.
d) Querying data in bound less ways.
e) Easy convenience and accessibility.
- Data lake has many tools and frameworks and some of tools and frameworks are Delta lake, database, Apache spark, Microsoft azure, presto and so on.
Role of data lake:
- Now, the world produces 90 percent of unstructured data like images, video, audio and so on. So, companies and organizations are working their business along with big data and features of data lake application for optimizing the business and improving the user experience.
- Analytics of data can permit the different roles of data lake for their respective companies like data developer, data scientist and analyst of business are ingress the big data with their tools and framework like Apache spark, Hadoop, presto and so on.
- Data lake has so many advantages but it also face the some of the challenges and the challenges like reliable of data, check the efficient performance of query.
Need of data lake:
- Companies can produce the data and finds their business process of value of that data and it is a source of type of data like social media and their message, audio, video and iot connect device are stack in data lake.
- Data lake helps the business to find and give the new opportunity Idea for quick growth of business by create the attention to the user, improve user experience, improve the production, decision making, improve the interaction with the user, improve the efficient operation and so on.
Visit here: Data Lake vs Data Warehouse
Contrast or comparison between data lake and data warehouse:
- Data can be stored in two ways and they are
- Data lake.
- Data warehouse.
- Compare the both data warehouse and data lake have most similarity but they are different in some of the areas.
- Data lake is a storehouse of raw data format store in their natural form, whereas data warehouse stores the processed data in structured.
- Data lakes can permit the data in the different types of data as we know, whereas data warehouse permit only the structured data.
- Data lake is a schema on read data type, whereas data warehouse is a schema on write data type.
- Scaling the data in data lake hold huge data amount with reduced cost, whereas data warehouse has more expensive data because of the buyer’s cost.
- Data lake can be used by the data scientist, data developer and business analyst, whereas data warehouse can be used by the data analyst.
ESSENTIAL COMPONENT OF DATA LAKE
- Data lake has many essential component are secure the data, quality of the data, discovers the data, stores the data, explore the data, ingest the data, analytics of the data and their techniques and so on. Some of the components are
- Movement of data.
- Secure and quality of data.
- Machine learning techniques.
Movement of data:
- Data lake stores more or huge amount of data which can be produced by throughout the world. It collects and organizes the different types of data that can be stack into data lake in their natural format.
Secure and quality of data:
- Data lake permit to stack the structured data like the database of operations and analysis of data and also stores the unstructured data like message, audio, video, apps and so on. It ensures that the data would be secure with their data values. Quality of data depends on the required constrain for the user to get business value from the data lake.
- It is extreme accessible and elasticity for the users to analyze the data lake with the business analyst, data scientist and data developer.
Machine learning techniques:
- Data lake permits the companies and organizations for to produce the sets of perception for the report of the actual data. In data lake use the machine learning techniques in an analytics of real life time application for predicting the expected outcomes of solutions in optimized way.
Why data lakes are so important because it has high speed, possess the authentic of data, easy convenience and elasticity, it can stack and analyze the data and also used for constructing the application of data. Data lakes are especially intended to roll on the industry of large scale for analytics of data with reduced cost manner.