Difference between Data Lake vs Data Warehouse: A Complete Guide For Beginners with Best Practices

• In this article you will learn:
• 1.What is Data Lake?
• 2.What is Data Warehouse?
• 3.A Difference between the Data Lake and Data Warehouse.
• 4.Data Lake Tools.
• 5.Data Warehouse Tools.
• 6.Conclusion.

What is Data Lake?

A Data Lake is a large storage repository that can hold structured semi-structured and unstructured data. It is the place to store any type of data in its native format with no restrictions on account size or file size. It offers a large quantity of data for improved analytical performance as well as native integration.Data Lake is a large container similar to a natural lake or riverLike a lake a data lake has many streams that flow into it. Structured data, unstructured data, machine-to-machine communication and logs all flow into a data lake in real time.

What is Data Warehouse?

A data warehouse is a collection of technologies and components used to make strategic use of data. It collects and manages data from various sources in order to provide useful business insights. It is a large amount of information stored electronically for query and analysis rather than transaction processing. It is the transformation of data into information.

A Difference between the Data Lake and Data Warehouse:

Parameters	Data Lake	Data Warehouse
Storage.	In a data lake all data is kept irrespective of a source and its structure. Data is kept in its a raw form. It is only transformed when it is a ready to be used.	A data warehouse will contain data extracted from transactional systems or data containing quantitative metrics and their attributes. The information is being cleaned and transformed.
History.	The use of big data technologies in data lakes is relatively recent.	Unlike big data the concept of a data warehouse has been used for decades.
Data Capturing.	Captures all the kinds of data and structures semi-structured and unstructured in an original form from source systems.	Captures structured information and organizes them in a schemas as explained for a data warehouse purposes.
Data Timeline.	Data lakes can retain all the data. This includes not only a data that is in use but also data that it might use in a future. Also data is kept for all the time, to go back in a time and do an analysis.	Significant time is spent during the data warehouse development process analysing various data sources.
Users.	A data lake is ideal for users who perform in-depth analyses. Included among these users are data scientists who require advanced analytical tools with capabilities such as predictive modelling and statistical analysis.	Because it is well structured easy to use and understand the data warehouse is ideal for operational users.
Storage Costs.	Data storage in big data technologies is less expensive than data storage in a data warehouse.	The storage of data warehouses is more expensive and time-consuming.
Task.	Data lakes can be contain all data and data types it empowers a users to access data prior a process of transformed, cleansed and structured.	Data warehouses can provide the insights into a pre-defined questions for a pre-defined data types.
Processing time.	Data lakes empower a users to access data before it has been transformed cleansed and aslo structured. Thus it allows users to get to result more quickly compares to a traditional data warehouse.	Data warehouses provide a insights into pre-defined questions for pre-defined data types. So any changes to a data warehouse needed more time.
Position of Schema.	Typically a schema is explained after data is stored. This provides high agility and simple of data capture but requires work at a end of the process.	Typically schema is explained before data is stored. Requires work at a start of a process but offers performance, security and integration.
Data processing.	Data Lakes use of an ELT (Extract Load Transform) process.	Data warehouse uses the traditional ETL (Extract Transform Load) process.
Complain.	Data is kept in its a raw form. It is only transformed when it is a ready to be used.	The chief complaint against a data warehouses is an inability or the problem faced when are trying to make change in them.
Key Benefits.	They integrate various types of data to come up with an entirely new questions as these users not likely to use a data warehouses because they can need to go beyond its capabilities.	Most users in organization are operational. These type of a users only care about the reports and key performance metrics.

Data Lake Tools:

Azure Data Lake Storage – Creates single unified a data storage space. The tool offers a advanced security facilities, accurate data authentication and limited access to a specific roles. Ideal for a large scale queries .

AWS Lake Formation – Offers a very simple solution to set up data lake. Seamless integration with an AWS-based analytics and machine learning services. The tool creates meticulous searchable data catalog with the audit log in place for identifying a data access history.

Qubole – This data lake solution are stores data in an open format that can be accessed through a open standards. Ad hoc analytics reports and the mixing of data pipelines to provide a unified insight in real-time are key features.

Infor Data Lake – Collects data from various sources and ingests into a structure that can immediately begins to derive value from it. Data stored here will never turn into the swamp due to intelligent cataloging.

Intelligent Data Lake – This tool helps a customers to gain maximum value from a Hadoop-based Data Lake. The underlying a Hadoop system ensures users don’t need much of coding for running a large-scale data queries.

Data Warehouse Tools:

The selection of tools and software is a critical factor in deciding between a Data Lake and a Data Warehouse:

Amazon Redshift – A cloud data warehousing tool that is an excellent for more speed data analytics. This data warehouse example can execute a numerous concurrent queries without the any operational overhead.

Microsoft Azure – It is the node-based platform that allows a massive parallel processing which helps to extract and visualize business insights much quickly.

Google BigQuery – This data warehousing tool can be integrated with the Cloud ML and TensorFlow to build a powerful AI models.

Snowflake – It allows analysis of a data from various structured and unstructured sources. It consists of the shared architecture which separates storage from a processing power. As a result users can scale a CPU resources according to user activities.

Micro Focus Vertica – This SQL data warehouse is aslo available in a cloud on platforms including AWS and Azure. It offers a built-in analytics capability for a machine learning, pattern matching and time series.

Amazon DynamoDB – Scalable DynamoDB can scale a querying capacity up to 10 or 20 trillion requests in the day over petabytes of data.

Conclusion:

A data warehouse is a large repository of organizational data gathered from a variety of operational and external data sources. The data has already been structured, filtered and processed for a specific purpose. Data warehouses pull processed data from various internal applications and external partner systems on a regular basis for advanced querying and analytics.