Cloud-based data lakes are ideal for exploratory and experimental data analysis, querying data on an ad-hoc basis. Along with that, it provides easy access to wide-ranging and massive-scale data. There is no setup time when data lakes have built-in indexes. In a data lake, you can retain any amount of data that has a great economical benefit. The other reason for it is the public cloud storage costs are so low. Now the what is a data lake, and what are its benefits?

Data lake and its benefits

As a lake caters to all kinds of users with its natural water, a data lake also serves different purposes with unrefined data. Using those stored data, a data scientist can perform the hypothesis based on it. An analyst can analyze the data to find the pattern. A business user can explore the data based on those reports.

The architecture of Data Lake is like “Hub and Spoke.” Thus Data Lake enables analytics as a service through this “Hub and Spoke” architecture. The following benefits of Data Lake enable the data science team to exploit analytics. It helps to develop the prescriptive and predictive analytics necessary to optimize key business processes. Also, it supports the data science team to explore, discover, learn, and refine the customer engagement model.

Related post – Why is Data lake a Viable Solution in Big data space?

Benefits:

• Data Lake is a centralized schema-less data storage. So it can store raw (as-is) data. As a result, we don’t need to worry about the format of the data.
• Due to schema-less architecture, rapid data ingestion with appropriate latency is possible in Data Lake.
• A Data Lake not only provides faster ingestion but also executes batch processing at scale.
• Data Lake supports most of the analytics sandboxes like Hadoop, Spark, Hive, HBase, HDFS, etc.
• It supports most of the Analytical tools (SAS, R, H2O, Mahout, MADlib)
• It supports most of the Visualization tools (Tableau, ggplot2, DataRPM)
• Data Lake supports most of the Big Data processing tools (MapReduce, YARN, Elastic Search, HAWQ, SQL)
• It has the ability to map data across sources and also provides visibility and security to users
• It provides the ability to manage data security, permissions, and masking.
• Supports self-provisioning of compute nodes, data, and analytic tools without IT intervention
• Data Lake encourages a common data model. Here, the business meaning of data is important than its physical structure and underlying technologies.
• As data is stored in its ‘as-is’ format, so there is no need to revise the existing data model or to redesign the database.
• We can source and connect external data in a real-time manner.

How data lake is important for business?

If a company retains a unified data lake in its public cloud instance, users can search and query as per their needs with unlimited data retention. Besides, they can analyze the data and obtain insights through proper visualization. It helps them to get quick answers based on which they can take action.

For example, log analysis is a data lake use case where businesses generate enormous logs, which is basically data about the IT system performance and cybersecurity events. The amount may be as large as terabytes. These logs are critical for keeping businesses running and also help to mitigate cyberattacks. Now, where does data lake sit here? You can push those logs into a data lake, and users can access those logs to analyze them in real-time. Most importantly, you can retain those data as long as you can if you want to recognize the pattern and analyze the trends.

The data lake has immense importance in the marketing domain. With a data lake, you can quickly turn an ever-changing collection of potentially large datasets about customers and their interactions with your company into insights that sales teams need. They allow for a shorter time to value thanks to just-in-time exploration and modeling on data pre-populated in the lake, made possible by data discovery and data integration tools now available on big data environments.

As we have discussed, the primary motivation of creating data lakes is to maintain any data that may be relevant for analysis on a given domain, now or in the future. However, the datasets ingested in raw format are not consumable directly. But the main goal is to defer the activity of consumption until you explore and discover a subset of the lake which is worth analyzing and validating a business hypothesis about the data. Once this is validated, you can prepare and model this data lake subset in a data schema to support a more complete analysis. This is more commonly known as “schema on read.” It helps to hold the preparation and modeling until we get a business reason for using the data. This is a game-changer as, without any engineering, you can prepare the lake to support the marketing campaign. This can be compared with data warehouse and its “schema on write” feature.

Use of Data Lakes to Improve Business Intelligence

Using BI, a company can use advanced methodologies to work with large volumes of raw data. This is an efficient approach for handling data. Using BI, you can retrieve meaningful insights, which improve decision-making and unearth new opportunities for business growth.

The role of BI solutions across various industries is hard to overstate. BI has become a top priority in the fields of:

• Finance and insurance
• Logistics and supply chain
• Health care
• Marketing and advertising
• Telecommunications
• Computational capabilities and cloud technology offer ways to process big data, reinforcing the digital transformation of those industries. 
• A data lake can enhance a BI solution by providing a greater potential for processing data. It can both serve as a centralized source of data for building a data warehouse and function as a direct source of data for BI. 
• Data lakes have applications in data science and machine learning engineering, where massive datasets are the backbone of technical solutions. In sum, a data lake can become an important pillar of BI and assist in optimizing raw data processing.