Data plays the role of fuel in our everyday tasks. The best example to understand is through an example of tech giants such as Google, Amazon, and Facebook; they are data-driven juggernauts taking over traditional businesses by leveraging the power of data. From government organizations, insurance companies, non-profit corporations to educational institutions, data is seen as a game changer. Despite the power, there are times when data is bad or causes bad decisions in statistical and machine learning models. To overcome these challenges, a revolution is occurring in how data is stored, processed, managed, and shared with decision-makers. It is the big data technology that is enabling scalability and cost-efficiency of data-related projects more than what is possible with traditional data management infrastructure.
Now, Data Lake is an approach that harnesses the power of big data. Today, the maximum number of enterprises have either deployed or are in the process of implementing data lakes within their ecosystem. But what exactly is a Data Lake?
You know it or not, data needs a home, and Data Lake is a preferred solution for creating that home. Just like Water Lake, it is a large body of data stored in a natural state. Within the lake, the data can be stored in any form, natural or raw data. From there, it can be accessed by various users or large user communities.
Going further, the article will talk about how Data Lake is built for the use of a large community of business analysts than just IT-driven projects.
What is data lake maturity? How is data lake different from the rest?
A data lake has multiple phases of maturity. The data tends to mature when the organization utilizes the data they produce. The more they do with their data, the more mature data they have, and consequently, getting higher on the scale of maturity. It is a new concept that determines the stages of maturity you might observe for your organization.
Data Puddle is a single purpose or single project data mart built using big data technology. The data loaded in the data puddle is for a single project or team.
It is a collection of data puddles. The data pond might look like a poorly designed data warehouse that will get data from other colocated data marts or might be the result of an offloaded data warehouse. The usage is limited because it is available only for the project that requires it. Owing to its high cost and limited usage, it does help in democratizing data usage or driving self-service, or decision making for businesses.
It is different from the above in two ways. Firstly, it supports self-service wherein business users can find and understand the required data easily without taking help from the IT department. Secondly, it can meet broader data needs as it contains data for general use, which is not limited to the use of a particular project.
It expands self-service data and decision making processes to all the enterprise data, wherever it may be, regardless of the data being uploaded in the data lake or not.
What are the factors needed for a successful data lake?
For any business project to be successful, it should firstly align with the company’s strategy and have the required executive sponsorship. Also, several companies have listed their varying levels of success, and for that, there are three prerequisites:
- The right data
- The right platform
- The right interfaces
The right data
The maximum part of the collected data is put into the trash. Only a small percentage is aggregated and kept in a warehouse for a few years. However, the storing and throwing of data makes it difficult to do the analytics. After throwing the data, one might want to reconsider the same, which might take a lot of time to assemble the history of data.
So, a data lake is also about saving the data for future use in the native format. One might not need it immediately, but the future is unseen. It also creates a possibility of sharing it within groups. A well-governed data lake is centralized and offers a transparent process throughout the organization on obtaining data.
The right platform
The right platform is a thing that can store a significant amount of data inexpensively. For example, big data technologies such as Hadoop and cloud solutions similar to Amazon Web Services (AWS), Microsoft Azure, and Google Cloud are the most popular platforms for a Data Lake. Also, these platforms are able and designed to scale out without any major degradation in performance. Unlike other platforms, Hadoop and similar platforms are very modular. The same file can be used by various processing search engines and programs.
The right interface
Most of the companies fail in choosing the right interface even after choosing the right platform and loading the data. It is required that the solution companies should become self-service, wherein users can easily find and understand the data without taking help from IT. There are two major aspects of enabling self-service; one is providing data at the right level of expertise, and the other is ensuring that users can find the right data.
Providing the right data to the right individual
For data scientists and data analysts to use the data, it has to be harmonized. It should be put into the same schema with the same field names and units of measure. On the one hand, the analysts want “cooked data” and not “raw data.” On the other hand, the data scientist can’t get the right extraction from cooked data. They need raw ingredients to create accurate analytical results.
There are a majority of companies that tend to settle on the “shopping for data” pattern. And the interface looks similar to Amazon.com, like an interface where users can easily find, understand, and consume data. The advantages of this approach include a familiar interface, which is available during online shopping—large and wide amount of search results, where easy ranking and sorting are possible, and with smarter catalogs.
What are the steps to make a successful data lake?
The process of making a successful data lake is very simple once all the factors are combined. It just takes the following steps to make one:
1. Create the right data infrastructure by getting the Hadoop cluster and run it.
2. Systematic creation and division of Data Lake wherein all the data is located as it will be used by user communities.
3. Set-up the data for self-service of users, such as giving access and providing tools for analysts to use.
In a nutshell, getting the right platform, right data, and right interface and setting it up for self-service are the keys needed for creating a successful data lake. With its ability to accommodate huge data volume, data quality of the complete enterprise, Data Lake works best for data scientists and data analysts.