Data is the fuel to boost your business’s profitability and to make more informed and smart decisions. A data lake is a fantastic concept that can be a game changer for your business if you learn to leverage its true potential. It’s a centralized repository where you can store and process massive data of all kinds, including structured, semi-structured, and unstructured. Amazon is the largest provider of cloud-based data lake solutions, with Google and Microsoft as competitors.
This post highlights some major benefits of using a data lake as a cost-effective storage option. Additionally, we will focus on the advantages of using the data lake from Amazon Web Services and shed some light on why modern-day businesses are switching to it globally.
- Key Components of a Data Lake Architecture
- Benefits of Amazon S3 Data Lake
- Best Practices to Approach AWS Data Lake
- Businesses Across the Globe are Getting Inclined to Data Lakes
Key Components of a Data Lake Architecture
Data lakes accumulate massive raw data in a native format, and each data element is tagged with a set of extended metadata tags. The basic difference between a data lake and a data warehouse is that a data warehouse has a hierarchical structure storing data in a structured form. In contrast, a data lake has a flat structure. The main advantage of using a data lake over a data warehouse is that a data lake can store both relational and non-relational data and help you know the type of data you have stored by applying techniques like indexing, crawling, and cataloging the data.
Whenever a business puts in a query related to anything in the data lake, it analyzes the data set. It brings out the most relevant information pertaining to the input or query.
Any data lake has a few key components that are aligned to ensure data aggregation and analysis. Here, we’ll discuss the four key components of the AWS data lake.
AWS data lake uses tools like Fluentd, Amazon Kinesis, Firehose, AWS Glue, and AWS storage gateway to extract data from a variety of data sources and load it into the lake.
The data (relational & non-relational) that you have in your AWS data lake is stored in S3 storage buckets.
Data Indexing or Cataloging
Indexing or cataloging the data in your data lake makes it visible or easily findable for users.
A data lake is linked to the analytical tools in the data pipeline and allows easy searching of desired data for enhanced insights.
Benefits of Amazon S3 Data Lake
For any business, it is imperative to have a robust analytical system comprising a database and data processing software along with web services. Amazon’s data lake architecture is a great solution to Big Data challenges that has enabled organizations to store, process, and leverage data intuitively and gain insights into processes, trends, and behaviors.
Here’s what you get when you switch to utilizing the Amazon S3 data lake:
Innovate for the Future
A data lake is a boon for research & development teams as it offers refined assumptions and data-based logic to them. It can help the team understand various attributes related to consumers, product design, quality, and metrics required to boost performance. You can ask your team to work on further improvements based on consumers’ feedback and the patterns they adopt to approach your specific products and services.
Enhanced Customer Service
Customer service remains a critical element in boosting sales, and the more secure information you will have, the better customer service you can offer to your customers.
A data lake can easily combine consumer data from CRM and other social media platforms. It allows easy data storage and facilitates running analytics on machine-generated IoT data. It also lays down a robust foundation for gaining critical insights into consumers’ purchasing patterns and complete buying history to identify consumers who are most likely to be converted into loyal customers. It helps you prepare data-driven marketing strategies to gain instant and optimum results.
Improved Operational Efficiency
IoT devices process a great deal of data collected by multiple sensors for different users and devices. This data can be collected, stored, and processed in a data lake. Businesses get quick access to this critical data to enhance manufacturing, sales & forecasting, supply chain, order management, and many others. By applying machine-generated data analytics on a data lake, you can significantly lower costs on operational processes and improve quality and productivity. You must deploy a suitable predictive model to make the most of analytics. It will undoubtedly maximize the accuracy of data to be analyzed to streamline various processes.
Streamline Diverse Datasets
Data management is a time-taking and monotonous task. You may collect data from diverse sources, due to which data structure and information may vary greatly. This makes it much more tedious to separate different datasets, categorize them, and analyze them to gain insights.
You can import as much data with a data lake, allowing easy scaling of data of any size, saving time in defining data structures, schema, and transformation. Just ingest any data in real time and build links for information labeled differently but containing the same thing. This ensures getting the right data to the right people at the right time.
Amazon Simple Storage Service (S3) data lake solution is a comprehensive storage platform that scales up its space according to your business’ growing needs. You can increase your storage capacity from gigabytes to petabytes by paying for the space you use. It offers native encryption of the data, access control capabilities, and various other user-friendly features that add to your convenience and saves time. The best part is you can integrate various other Amazon services as and when required. This also lays a robust foundation to implement technologies like AI and ML, which work on structured and unstructured datasets to offer valuable insights.
Best Practices to Approach AWS Data Lake
To make the most out of an AWS data lake and gain optimum results from data analytics, you must approach this concept strategically. Below are some standard practices you must consider:
Storing Raw Data in Its Source Format
Ensure that your AWS data lake is configured to ingest and store raw data in its source format. This benefits analysts in adopting innovative ways to query or generate relevant use cases for enterprise data. Moreover, storing data in its raw format means no loss of information, and you can use it as and when required.
Use Amazon S3 Storage Classes for Cost-effectiveness
You get different cloud storage classes with Amazon S3. The intelligent tiering (frequent, infrequent, archive, and deep archive) of S3 is well-organized, where you need to pay less for the data used less frequently. If you aim to store historical data long-term or minimize costs for data retention or audit purposes, you can opt for Amazon S3 Glacier.
Get Better Control with Data Lifecycle Policies
Your DevOps team can effectively manage and control data flow by implementing data lifecycle policies. These policies can be selected depending on objects entering S3, transferring them to cost-effective storage classes, or deleting the useless data with null viability.
Use Amazon S3 Object Tagging
AWS data lake stores massive data, and object tagging can help you categorize objects to get relevant search results. These object tags are referred to as “key-value pairs,” defining the specific characteristics of the objects. You can assign a maximum of 10 tags to each object. This helps you recreate data across different sections and filter objects with the same tags for analytical purposes.
Businesses across the Globe Are Getting Inclined to Use Data Lake on AWS
Data-driven organizations from different industry verticals are making optimum use of AWS data lake as it allows them to perform intuitive analytics, including SQL queries, Big Data analytics, and machine learning-based real-time analysis. The best part is that a data lake supports all data formats. Moreover, data can be stored from a long-term perspective to get better quality data for enhanced planning and overall management.
Data storage in native format and the high-level scalability offered by data lakes make them a firm choice for growing businesses that look for versatility and flexibility in data storage. So, you can easily boost your advanced analytics with AWS data lake and get seamless access to valuable insights to take your business to the next level.