Data Lakes and Data Warehouses are used widely to store large amounts of data. However, they are not interchangeable terms. You will be surprised to know that both of these approaches are complementary to one another. Let’s know about these two terms deeply in the segments mentioned below.
Introduction to Data Lake
Data Lake is known to be a depository that is centralized. It enables you to accumulate all your given informed and unformed data. One of the best things is that it does that at any scale. It allows you to store your data unstructured and ideate various types of analytics. From visualizations and dashboards to the big procession, machine learning guides you towards better decisions.
Data Lake is less structured, more like a lake where you dump everything first then find out usage later
Why does an enterprise need a data lake?
Organizations and firms that successfully business worth from their data, will overdo positively their peers. In various surveys, it is noted that plenty of organizations that executed a Data Lake outperformed familiar companies by 10% (approx.) in authentic revenue increase. These firms managed to do unique sorts of analytics like data from clickstreams, social media, log files, and internet-corresponding devices stowed in the Data Lake.
Ultimately, it helped them to recognize and act on the opportunities for the faster growth of the business by retaining and attracting customers, increasing productivity, and making instructed decisions.
What value does a data lake hold in an enterprise?
The capability to store plenty of data, from tons of sources, in minimum time and empower users in order to ask them to unite and examine data in various ways often directs to better and quicker decision making. The following are some instances that will make it clear for you:
- Enhanced Customer Interplay:
A data lake has the ability to combine all the client data through a CRM platform. It happens to do so with social media analytics. Then it creates a marketing platform that consists of purchasing history and ‘’happening’ tickets in order to delegate the business to acknowledge the most valuable and promising client cohort, the reason behind client churn, and the rewards and other promotional activities that will improve the loyalty in them.
- Enhanced R&D Innovation Choices:
Data Lake enables your R&D squads to examine their thesis, refine inferences and analyze results accordingly. It can include selecting the suitable materials in your product creation that results in quicker performance, genomic research eventually leading to improved medication.
- Improved Functional Efficiencies: The IoT presents various ways to collect data on processes like production, with live data incoming from internet-connected devices. Ideally, data lake happens to make it much easier to store and run the analytics on IoT data (machine-generated) resulting in reduced operational cost and improved quality.
Positioning Data Lakes in the Cloud
Essentially, Data Lakes are an exemplary workload that happens to be cloud-deployed, as the cloud introduces implementation, dependability, scalability, availability, and a unique and categorized set of analytics engines.
Moreover, the major reasons clients perceived the clouds as an edge for Data Lakes. It is so due to better security, quicker time to availability, deployment, often feature updates, geographical coverage, elasticity, and costs connected to existing utilization.
A good example for a Data Lake is Google Cloud Storage or Amazon S3
Introduction to Data Warehouse
Data Warehouse is a central repository of information that is enabled to be analyzed in order to make informed decisions. Typically, the data flows into a data warehouse from transactional systems and other sources.
Data Warehouse is more structured, more like a water tank where you define usage first then put in the data
How does a data warehouse work?
You may find multiple databases in a data warehouse. Each database has its own data which is organized into tables and columns. And each column, a description of the data is enabled to define accordingly. On the other hand, the tables can be organized inside schemas, which can be known as folders. Finally, when the data is ingested, it is simply stored in different tables.
Why is Data Warehouse important?
Data Warehouse holds a great value when it comes to informed decision-making like Data Lake. not only this but it also manages to consolidate data from plenty of sources. In addition to this, historical data analysis, data quality, accuracy, and consistency are some of the elements data warehouse comes with. Furthermore, the separation of analytics processing from transnational databases ultimately enhances the performance of both the given systems
A good example for Data Warehouse is Google's Big Query or Amazon Redshift
Data Lakes and Data Warehouses have two different approaches- Here’s how
Depending on the concerned needs, an organization will need to have a data warehouse and a data lake because they offer diverse needs and use cases.
A data warehouse is quite different from a data lake. A data warehouse is a database optimized in order to analyse relational data arriving from transactional systems and lines of enterprise applications.
On the other hand, a data lake serves different purposes as it stores relational data from a line of enterprise applications. The difference is that it stores data from mobile applications, social media, and IoT devices as well. Meaning, it stores all of your given data without any careful design.
Moreover, data warehouses are primarily used for batch reporting, visualizations, and BI analytics on structured data. Whereas data lake can be potentially be used for solving problems of machine learning, data discovery, predictive analytics, and profiling with large amount of data
Organizations with data warehouses happen to see the perks of data lakes. To maximize their benefits, they are evolving their warehouse to include data lakes as well. Not only does it ensure diverse query abilities but advanced abilities for discovering new info models as well
How do data lake and data warehouse work together?
These approaches are complementary to one another. Data warehouse manages to structure and package the quality of the data, consistency, and performance with significantly increased concurrency. On the other hand, data lake ensures to focus on original raw data commitment and permanent storage. It does that at a reasonable cost while offering a new state of analytical dexterity.
These two different yet complementary solutions are recommended to be a part of any