What is Data Lake?

 

A data lake is a repository that holds a vast quantity of raw data in its native format until its demanded analytics operations. While a traditional data storehouse stores data in hierarchical confines and tables, a data lake uses a flat armature to store data, primarily in lines or object storehouses. That gives users more workability on data operation, storehouse and operation.

Data lakes generally store sets of big data that can include a combination of structured, unshaped, and semi-structured data. Such surroundings are not a good fit for the relational databases that most data storage is made up of. Relational systems bear a rigid schema for data, which generally limits them to storing structured sale data. Data lakes support colorful schemas and do not want any to be defined as outspoken. That enables them to handle different types of data in separate formats.

Data lake architecture

A data lake has a flat architecture because the data can be unshaped, semi-structured, or structured, and collected from random sources across the association.

Because of their architecture, data lakes offer massive scalability to any organization. This is important because when creating a data lake you generally don’t know in advance the volume of data it’ll need to hold. Traditional data storehouse systems can’t gauge in this way.

This architecture benefits data scientists who are capable of mine and exploring data from across the enterprise and sharing and sourcing data, including miscellaneous data from different fields, to ask questions and find new perceptivity. They can also take advantage of big data analytics and machine learning to dissect the data in a data lake.

Indeed though data doesn’t have a fixed schema precedent to a storehouse in a data lake, data governance is still important to avoid a data swamp. Data should be tagged with metadata when it’s put into the lake to ensure that it’s accessible latterly.

Advantages of Data Lake

High Scalability

Scalability refers to a data system’s, network’s, or process’s capacity to handle adding quantities of data, as well as its capability to expand to accommodate that growth. When Data Lakes ’ scalability is taken into account, it’s relatively affordable when compared to a standard Data Warehouse.

Supports numerous Languages

Traditional data-storehouse technology primarily supports SQL, that’s forfeiture for introductory analytics, but we need other ways to examine data for sophisticated use cases. But the Data Lake provides numerous numbers of languages.

Advanced Analytics

Unlike a traditional Data storehouse, Data Lake excels at relating objects of interest that will enable real-time decision Analytics by combining massive quantities of coherent data with Deep Learning algorithms.

Some best practices of Data Lake

1. Identify the Business Problem

Numerous organizations fail to apply a data lake as they haven’t linked a clear business case for it. Organizations that begin by relating a business problem to their data are more likely to be effective. As the volume and variety of data continue to grow, businesses find it difficult to store, manage, and process the data at the speed needed for timely action.

2. Find the Right Resources

Data lake executions are a company-wide, strategic right-of-way involving entities from different departments, not just the IT crew. It’s important to have the right people and crew in place for the execution. Companies frequently don’t completely understand how numerous resources it takes to make a data lake, because it’s all new to them.

3. Cleanse Your Data

Data cleansing improves the data quality by relating and correcting errors before the data is moved to the data lake. Both Man-made and automatic data cleansing involve a series of the way — correcting any mismatches, recreating missing data wherever possible, reordering rows and columns, assuring that data is in the same format as the destination, and deleting indistinguishable records.

4. Secure Your Data Lake

You need to ensure that your data is securely managed. Some companies face security issues because they misconfigure authorizations and make their data fluently accessible. Numerous tools and technologies can help with security and governance.

We hope this article was insightful and helped you to understand the concept of Data lake. Thank you for showing interest in our blog and if you have any questions related to Data Lake, Big Data, or AI-based platforms, please send us an email at info@futureanalytica.com.