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Data Lakes Reinvented New age Applications

The data lake solutions has earned a wide reputation in the latest years. It boasts of the modern design pattern which is capable of fitting the data of the latest time. This pattern is useful to the potential audience in using and organizing the data. 

For instance, a wide array of business enterprises intends to incorporate the data faster into the lake. Hence, employees of the organization can avail the data for analytics and operation. 

The organization aims to store the data in the raw and original state, which offers a helping hand in processing it in various layers. It brings a revolution in the operations of business analytics. They want to capture the unstructured data, big data and other data from different sources.

The potential audience is under tremendous pressure for creating an organizational advantage and business value from different types of data collections through different discovery-oriented analytics. 

A data lake provides assistance with all the needs and trends as the potential audience can resolve all the challenges of the data lake. As data lake is totally new, the design patterns and best practices are still a bit baffling. 

Here are some of the primary applications of a data lake which are beneficial to the data management professionals along with the business counterparts.

Incorporation of data faster with no or less front improvement

Early ingestion and late processing happen to be an integral part of the data lake. As you opt for the late processing and early ingestion practice, it offers the suitable choice to integrate to the data for the reporting, operations, and analytics. 

It demands different diverse ingestion techniques for handling diverse interfaces, data structures, and container types which are useful in scaling to real-time latencies and massive data volume. It is also useful in simplification of the onboarding of data sets and data sources.

Persisting the data into the raw state for preserving the original schema and details

Detailed source data is known to be preserved into the storage. So, you will be capable of repurposing the data continuously with the upcoming of new business needs. In addition to this, raw data is necessary for discovery-oriented analytics and exploration. This data works wonders with detailed data, large data sample, and data anomalies.

Controller the loading of data into the lake

If the data is not controlled, there are risks that it might get converted to the data swamp. Due to this, the data lake becomes an undocumented and disorganized data set which cannot be leveraged, governed and navigated easily. You can make the best use of policy-based data governance for seeking control. The data curator and steward enforce the anti-dumping policies into the data lake.

Also, the policies provide exceptions as the data scientist, and data analyst throws the data in the analytics sandboxes. You need to document the data, once it goes into the lake with the aid of the business glossary, information catalogue, metadata, and different semantics. It provides the choice to the potential audience for optimizing queries, finding the data, governing the data. It also plays an integral role in decreasing data redundancy.

Integration of data into the structure, diverse sources and vintages 

A wide array of the potential audience makes use of the modern big data and traditional enterprise data on the Hadoop-based data to increase the customer views, advanced analytics, enriching different cross-source correlations to seek insightful segments and clusters. Besides this, some business organization seeks the blended data lake to allow sentiment analysis, logistics optimization, predictive maintenance, to name a few.

Capturing big data and different new data sources within the data lake

An integral part of data lakes are deployed with the aid of Hadoop, whereas few of them are deployed on traditionally systems and Hadoop partially. There are a plethora of data lakes which handle big data since Hadoop contributes to being a perfect choice. Hadoop-based data lakes offer a helping hand in capturing massive data catalogue from a bunch of new sources.

Different architectural and technical purposes

A single data lake will be capable of fulfilling different architectural objectives which include data staging and landing. As Data Lake comes with a variety of architectural roles, it should be distributed in a bunch of data platforms, where each of them come with unique processing and storage features.

Improving and extending the new and old data architecture

A wide part of the data lakes are considered to be an integral part of the multiplatform data ecosystem. Common instances of such data lakes include omni-channel marketing, data warehouse environment, and digital supply chain. Also, different traditional applications of a data lake are inclusive of content management, financial, multi-module ERP, document archiving. The data lake is recognized to be the modernization strategy which is useful in extending the functionality of the data environment.

Choosing different data management platforms which accomplish data lake needs

Hadoop is considered to be the preferred data platform owing to the linear scalability, lower price and powerful for analytics. But, few potential audiences implement MPP or massive parallel processing relational database as the data lake needs relational processing.

Allowing self-service best practices

It is inclusive of data visualization, data preparation, data exploration and different types of analytics. A wide array of the savvy potential audience wants access to the data lake. Key components allow self-service functionality.

Hybrid platforms have become the latest buzz in the town. This data storage platform is beneficial in analyzing, processing and holding the unstructured and structured data. It is possible to use such a platform in combination with EDW and enterprise data warehouse. 

Such a data storage platform helps in saving an ample amount of money as business enterprises make use of easy to obtain and budget-friendly hardware. In the data lake, data are preloaded in the raw formats. Instead, they are preconfigured after an entry into the company systems. 

They are considered to be the combination of relational and Hadoop systems on the on-cloud and on-premises systems. With a wide array of data collections, the company finds a rise in cloud storage.