Every single day, people all over the world contribute to creating roughly 2.5 quintillion bytes of data. According to a study, there were 79 zettabytes of data generated worldwide in 2021. Now, most of this data is unstructured or semi-structured, which presents a major challenge - how to store all this data and maintain the capacity to process it quickly. And this is where data lakes come in.
Why Do You Need a Data Lake?
Data lakes are a central repository that allows you to store data at any scale. It can hold all sorts of big data in a raw and granular format. You can store any type of unstructured data and run different types of analytics on them. Data lakes are usually configured on inexpensive and scalable commodity hardware clusters. This makes it easier for data to be dumped into the lake without having to worry about structure and capacity. These clusters can exist in the cloud or on-premises.
Data Lakes Compared to Data Warehouses – Two Different Approaches
Data lakes are sometimes confused with data warehouses. Both provide huge benefits to organizations but they come with their own distinct differences.
Here are some of the major differences between them:
Relational data from operational databases, transactional systems, and business applications
Non-relational and relational data all types of sources
Written prior to the data warehouse implementation
Written at the time of analysis
Fastest query results using higher cost storage
Slower query results using low-cost storage
Highly curated data
Any data that may or may not be curated
Data scientists, Data developers, and Business analysts
Batch reporting, BI and visualizations
Machine Learning, data discovery, profiling, and predictive analytics
The Essential Elements of a Data Lake and Analytics Solution
When organizations build a data lake and analytics solution, they need to consider a number of key elements, including:
Data lakes allow you to import any amount of data in its original format that comes in through multiple sources in real-time. This allows you to save time in defining data structures, schema, and transformations.
Data lakes allow you to access and run analytics on data without the need to move your data to a separate analytics system. This includes open-source frameworks as well as commercial offerings from data warehouses and business intelligence vendors.
Securely Store and Catalog Data
Data lakes allow you to store both relational and non-relational data securely. It also gives you an idea of what data is in the lake through cataloging, crawling, and indexing of data.
Data lakes allow you to generate different types of insights and perform machine learning on data to forecast likely outcomes and suggest prescribed actions to achieve the optimal result.
The Value of a Data Lake
The ability to harness enormous amounts of data from multiple sources in real-time has empowered users to collaborate and analyze data for better and faster decision-making. Here are some areas where data lakes have contributed value to:
- Improved customer interactions
- Improve R&D innovation choices
- Increase operational efficiencies
Architecture of Data Lakes
A data lake architecture refers to the features included within a data lake that makes it easier to work with that data. Even though data lakes are designed to contain both structured and unstructured data, it is still important to ensure that they offer the functionality and design features to easily interact with the data inside them.
Here are some best practices you can use while building a data lake:
1. Establish Governance
Data governance refers to standards that organizations use to ensure that data fulfills its intended purpose. It also helps maintain data quality and security. Including data governance into your data lake architecture ensures that you have the right processes and standards from the start.
2. Create a Catalog
A data catalog makes it easy for stakeholders within and outside your organization to understand the context of the data inside the data lake. The types of information included in a data catalog can vary, but they typically include items such as - the connectors necessary for working with the data, metadata about the data, and a description of which applications use the data.
3. Enable Search
While data catalogs enable you to find the data within the data lake, it is also crucial to search through the data lake. Because a data lake is usually huge, it is not feasible to parse the entire data lake for each search. Instead, build an index for fast searches in the beginning and rebuild this periodically to keep it up-to-date.
4. Ensure Security
Data security is crucial for ensuring that sensitive data remains private and adheres to compliance requirements. You can include rigid access controls and encryption in your data lake architecture.
The main challenge with data lakes is that raw data is stored with no inspection of the contents. In order to make the data usable, there should be defined mechanisms to catalog and secure data better. Without these essential elements, data can neither be found nor trusted which will result in a data swamp. To meet the needs of wider audiences, data lakes should have governance, access controls, and semantic consistency
Cloud Data Lakes or On-Premises?
Data lakes on-premises data enable organizations to have their own control over design, space and power requirements, management hardware and software procurement, the skills to run it, and ongoing costs. Outsourcing the data lake to the cloud has the advantage of offloading all these responsibilities to the cloud provider. Both offer their own benefits and a careful analysis of the benefits and drawbacks of each is needed depending on the organization.
Deploying Them in the Cloud
Data lakes are ideal to be deployed in the cloud because the cloud provides a number of benefits such as availability, scalability, performance, reliability, and massive economies of scale. According to ESG research, 39 percent of respondents consider the cloud as their primary deployment for analytics. The top reasons why they perceived the cloud as an advantage for data lakes are faster deployment time, better security, better availability, more functionality updates, more elasticity, and costs linked to actual utilization.
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Getting Started With Data Lakes
The rise of data has led to the increased usage of data lakes in multiple sectors. The question is no longer whether a data lake is needed for an organization, but it is about which solution to use and how to implement it. If you want to learn more about data lakes, you can check out Simplilearn’s Data Science Certification that features masterclasses by Purdue faculty and IBM experts. This Data Science program is ideal for all working professionals and covers a number of job-critical topics like R, Python programming, Machine Learning algorithms, and NLP concepts with live sessions by global practitioners, practical labs, IBM Hackathons, and industry projects. Get started with this course today and boost your career in data science.