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Big data is defined as data with greater diversity, higher volumes, and greater velocity. This is also known as the three Vs of Big Data.
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Last updated on Nov 21, 2023
Big data is defined as data with greater diversity, higher volumes, and greater velocity. This is also known as the three Vs of Big Data.
Volume: Big data has high volumes of unstructured data to be processed. For example, consider the billions of users on social media handles such as Twitter, Facebook, Instagram, etc., contributing billions of tweets, posts, and pictures, generating insurmountable amounts of data every day.
Velocity: Velocity refers to the speed at which the data is generated. Data today is generated at lightning speed. Consider the social media example above. Billions of people generate Terabytes of data every hour on these platforms.
Variety: Traditional data types used to be structured, making them easier to sort and compute. The advent of Big Data has changed the scenario with new unstructured data forms such as audio recordings, hand-written texts, voicemails, etc., also in the mix. This has mandated additional data processing to make some sense out of the seemingly unrelated data.
Therefore, we could say that Big data is simply larger, more complex data sets, particularly from new data sources. These enormous amounts of data can be leveraged to solve previously unsolvable business challenges. However, traditional data processing software cannot handle these massive data sets. Let us study the major challenges that Big Data pose:
Data Ingestion: The quantity and diversity of Big Data are immense. The collection of raw data from various sources (transactions, logs, mobile devices, etc.) is the first hurdle organizations face when dealing with Big Data. Developers require a platform that allows them to ingest structured and unstructured data from a wide variety of sources at high speeds.
Data Storage: Storing such vast quantities of data is no small feat. Any Big Data platform not only needs to store the data but also maintain security as well as act as a durable and scalable repository pre and post-processing.
Data Processing: The data gathered and stored until now are generally not used as no discernible meaning is visible in the raw data. Data processing such as sorting, aggregation, joining, and various other advanced features and algorithms are often required before one can begin to understand the data and interact with it, which again becomes a humongous task with such quantities of data.
Data Visualization: Perhaps the most significant advantage of Big Data lies in the prospects of the highly insightful and invaluable data obtained from its depths. But obtaining that information requires complex calculations and necessitates not only powerful computation but also presentation capabilities to allow business stakeholders to gain a better understanding of the information.
There is a clear need for a platform capable of handling vast amounts of data and tackling the above problems. This is where AWS steps in.
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An acronym for Amazon Web Services, AWS is a comprehensive cloud computing platform that provides a range of on-demand cloud-based products such as storage, computing, analytics, etc., suitable for businesses.
Using AWS services not only provides users with powerful computing solutions and the ability to handle large quantities of data but also allows them to save high costs and time by not having to invest in acquiring and maintaining hardware and infrastructure required for similar storage and computing capacities.
AWS operates in 25 regions across six continents. There are several availability zones in each region. These are the physical data centers where computers are housed and are geographically isolated to lessen the chances of a local tragedy affecting an entire region. In addition, AWS's content delivery network has over 200 edge locations worldwide (CDN).
AWS has even invented its proprietary hardware to make its network faster and more robust.
In addition to providing a host of services beneficial for Data analysis, AWS also makes sure that they are in line with new market trends by making frequent updates allowing for better handling and computations of data.
Some of the well-known platforms using AWS services include:
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So how would using AWS help solve the challenges posed by Big Data?
AWS provides various services that help a user tackle the various challenges faced at different stages involved in dealing with Big Data. Let us learn more about the AWS services used to solve the Big Data challenges we discussed earlier:
The following AWS services efficiently handle the ingestion of data at speeds ranging from real-time to batch:
Amazon Kinesis Firehose: Amazon Kinesis Firehose is a fully managed solution that delivers real-time streaming data to Amazon S3 (we will learn more about this service ahead) in real-time. Kinesis Firehose scales automatically to match the volume and speed of streaming data and does not require any continuous management. Kinesis Firehose can be used to alter streaming data before storing it in Amazon S3.
Amazon Snowball: You may use AWS Snowball to move large amounts of data from on-premises storage platforms and Hadoop clusters to S3 buckets in a secure and fast manner. A Snowball appliance is automatically created after creating a job in the AWS Management Console. Connect the Snowball device to your local network, install the Snowball client on your on-premises data source, and then choose and transfer the file directories to the Snowball device using the Snowball client.
Storage Gateway: The AWS Storage Gateway is a collection of hybrid cloud storage services allowing for on-premises access to physical storage and extracting data to the S3 Data Lake.
The below AWS services provide effective big data storage solutions:
Amazon S3: Amazon S3 is a safe, scalable, and long-lasting object storage service with millisecond latency. It can store any data form from multiple sources, including data from IoT sensors and devices. It can also store and retrieve any amount of data with unparalleled availability, and it was designed from the ground up to offer near 100% durability.
AWS Lake Formation: Organizations may create data lakes in a matter of days with AWS Lake Formation. A data lake is a centralized data storage service that stores data in raw and structured formats, ready for analysis.
AWS Glue: A fully managed data cataloging solution that makes data in the data lake discoverable. It also can Extract, Transform, and Load (ETL) data to prepare it for analysis. Furthermore, the built-in data catalog acts as a persistent metadata store for all data assets, allowing all data to be searched and queried in one place.
AWS RDS: AWS Relational Database (RDS) is used to create, manage, and operate relational databases. It also provides popular data engines such as MySQL, MariaDB, and Oracle.
As mentioned earlier, Processing Big Data can be a daunting task. The below listed AWS services can help make that significantly easier:
EMR: AWS Elastic MapReduce (EMR) is among the leading Big Data Tools in the industry and provides a managed service allowing for quick, easy, and cost-efficient processing of high-scale data
Redshift: Amazon Redshift allows analysts to conduct complicated analytics queries against petabytes of structured data for a fraction of the cost of standard processing solutions—close to one-tenth. It also includes a Redshift Spectrum that enables the Data analysts to run SQL queries directly against exabytes of either structured or unstructured data stored in S3, eliminating the need for wasteful data movement.
The following tool provides useful visualization techniques allowing for better presentations:
Amazon Quicksight: Amazon Quicksight provides stunning visualizations and great interactive dashboards that can be accessed from any mobile device or web browser. This business intelligence resource employs AWS' Super-fast, Parallel, In-memory Calculation Engine (SPICE) to run data calculations and generate graphs quickly
Big Basket is India's most popular online grocery store. According to Business Standard, it presently processes roughly 20 million orders each month and has an 84 percent user base in 2021. During the COVID-19 lockdown, this boom happened.
Big Basket has been using a variety of AWS services for years. Let's look at some of its core services and how they helped it deal with its rapid expansion.
AWS Redshift & AWS S3: The data warehouse and the data lake are managed via AWS Redshift and AWS S3, respectively. Big Basket uses these services to record consumer behavior to achieve maximum customer retention during the surge. The important insights are then used to improve the customer experience on the platform.
AWS Elasticsearch: Big Basket uses Elasticsearch for geo-analysis of multiple cities to better cater to local demand. As a result, the corporation may stock the inventory with the proper products based on local demand. Inventories are used to store the right products in the defined service area so that they are all immediately available.
AWS RDS: Big Basket has been using Amazon Web Services RDS for five years. The database began to increase significantly during the COVID era. As a result, RDS usage soared, as did the expense of using it. However, because RDS can be designed for scaling, the cost is dramatically reduced.
As a result, Big Basket has been able to handle the data spike for months using AWS services. The organization could handle the data flow smoothly with the correct scale and implementation of services.
In today’s digital age, data is the new oil. Managing Big Data may not only prove highly difficult but also very costly. Therefore, the importance of the ability to collect and utilize it in a cost-effective manner cannot be overstated. AWS is providing just that, allowing it to become the leading cloud-based computing platform and the central profit driver for Amazon Inc.
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Big data is defined as data with greater diversity, higher volumes, and greater velocity. This is also known as the three Vs of Big Data.