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Are Data Lakes And Data Warehouses The Two Sides Of A Modern Cloud Data Platform

A true cloud data platform is capable of providing a plethora of functions, which complement and overlap one another. The majority of the business organizations consolidate the data from different resources into the singular customizable platform for big data analytics. 

A separate platform for data analytics offers the prerequisite choice to create the dashboards for analyzing, aggregating, and segmenting high-dimensional data. It provides a helping hand in creating low-latency queries for performing real-time analytics. 

Data lakes and Data warehouses are also known to be common alternatives. Data warehouses and data lakes are believed to be the two different sides of the recent cloud data platform, which offers a wide array of benefits.

What is Data Lake?

Data Lake Solutions refers to a term which was introduced in the year 2011 by James Dixon, Pentaho CTO. It contributes to being the large data repository in the unstructured and natural form. 

Raw data is known to flow into the data lake. Also, users have the opportunity to correlate, segregate, and analyze various data parts, following the needs.

Data Lake depends on low-cost storage options, which are beneficial in storing the raw data. Data gets collected from different sources in real-time, after which it is transferred into the data lake in the original format. 

It is possible to update the data in the data lake in batches and real-time, thereby creating the volatile structure.

What is Data Warehouses?

A data warehouse contributes to being the central repository of the data, which is collected from a vast array of diverse sources, such as in-house repositories and cloud-based applications. 

The data warehouse makes use of column-oriented storage, referred to as the columnar database. The database is capable of storing the data by the columns and not by rows.

Hence, it is believed to be an excellent choice for data warehousing. If there is a data warehouse enriched with historical and current data, people in the business organization will use it to create trend reports and forecasting dashboards with the aid of different tools.

A data warehouse boasts of certain characteristics, which include scalable, structured, non-volatile, and integrated. The scalable data warehouse is capable of accomplishing the enhanced demands for the storage space. 

The structured data warehouse uses a columnar data store to bring an improvement in the analytical query speed. As the data present in the data warehouse is uploaded periodically, the momentary change does not affect decision making. 

The integrated data warehouse involves the extraction and cleansing of data uniformly, instead of the original source.

The data warehouse serves as the data-tier application known to define the schemas, instance-level objects, and database objects used by the client-server or three-tier application.

Data Warehouse and Data lakes- Two sides of the cloud data platform

Data lakes and Data Warehouses are recognized to be the two sides of the cloud data platform, which offers a helping hand in making an informed purchase decision. 

There are specific use cases that boast of a data analytics engine in which the data warehouse and data lake will co-exist. However, it depends on the area's different functional requirements, which include data adaptability, data structure, and performance.

Data Performance

As you aim to create a data warehouse, the data source analysis happens to be a significant time-consuming factor. It is useful in the creation of an organized and structured data model, which is meant for individual reporting needs. 

A crucial part of the process is deciding the type of data which should be included, and things, that need to be excluded from the data warehouses.

It includes the data collected from different resources, after which the data should be aggregating and cleansing. Also referred to as data scrubbing, data cleaning refers to the technique of data clean up. 

It happens before the data is loaded into the data warehouse. The objective of data cleansing is the elimination of outdated data.

After the completion of data cleansing, it is ready for analysis. However, it takes the prerequisite energy and time, owing to the sequence involved in data cleansing techniques. 

Data warehouse works wonders in cleaning the data. However, it is a bit pricey. A data lake includes relevant data sets, regardless of the structure and source. It is responsible for the data storage in the original form.

Data warehouses are created for the purpose of faster analytical processing. The columnar and underlying RDBMS provides accelerated performance, which is optimized for the purpose of analytical query processing. 

It is inclusive of high concurrency and complicated joins. However, it would be best if you keep in mind that data lakes are not performance-optimized. However, if you have any access to it, you will be capable of exploring the data at their own discretion, leading to a non-uniformed data representation technique.

Adaptability

A robust data warehouse has the ability to change faster and adapt to various scenarios. However, the data lake is faster to adapt to different changing requirements to adjust to multiple scenarios. 

The complications of the upfront tasks need the resources and time of the developer. Data Lake can adapt to other changing requirements owing to the fact that data is present in the raw and unstructured form. 

Such type of unstructured data is available to the potential audience, which has the power to use and access it for forming the analysis, catering to the requirements. The developers should devote and hump the resources and time, which is necessary to get meaningful information from the data.

Microsoft, Google, and Amazon confer Data Lake and Data Warehouse services, which offer platforms, against which the business organizations will run the BI reporting and analytics in real-time. 

Microsoft Azure and Amazon Redshift are developed on the relational database model's top. It also provides large-scale and elastic data warehouse solutions. Google Cloud Datastore contributes to being the NoSQL Database as a Service of SaaS capable of automatically scaling. Every data warehouse is equipped with BI tools, which are integrated into the services.