Big Data adoption is skyrocketing all over the world. According to Wikibon, this market is projected to grow to $103B by 2027. In five years, more than 150 trillion gigabytes of data will require analysis.
That’s a lot of data.
Companies are growing increasingly aware of how valuable all of this information can be for driving critical business decisions. Big Data insights allow organizations to identify new sources of growth, accelerate innovation, and mitigate risk.
However, taking advantage of modern analytical techniques requires much more than new-age software. Businesses need cloud-based infrastructure, sophisticated tools, efficient data pipelines, scalable repositories, and more to maximize potential.
At ClearScale, we’ve worked on our fair share of these projects. Our team has learned the essential big data best practices when it comes to designing IT infrastructure for data analytics. We specialize in the Amazon Web Services (AWS) platform, a leading cloud service provider that offers a vast portfolio of Big Data solutions.
Here, we discuss why following best implementation practices are so important when upgrading your analytical toolkit with AWS. We also highlight how AWS differentiates itself from other cloud providers to help you get started in the right direction.
Increase Long Term ROI
Many companies still rely on traditional analytical techniques that struggle to process large datasets efficiently. Replacing these legacy applications can be expensive, depending on the current state of your IT infrastructure. The right solution for your business will enable you to recoup your investment in multiple ways.
Big Data analytics allow businesses to identify unmet customer needs and new revenue opportunities that would otherwise go unnoticed in massive datasets. With Big Data insights, companies can reduce the total cost of ownership and labor related to managing legacy analytics applications.
When pursuing Big Data, you should also consider the opportunity cost of sticking with what you already own. Organizations often leave tremendous value on the table when they don’t invest in modern analytical tools.
Amazon Web Services offers an unparalleled suite of solutions for optimizing analytics spend. For example, as a fully managed ETL service, AWS Glue simplifies Big Data management significantly. The solution empowers users to remove duplicate data, find hidden patterns, and prepare massive datasets for analytics. Amazon Kinesis helps you collect, process, and analyze real-time data so you can get timely insights into new information. Amazon Redshift is a data warehouse solution you can use to acquire new insights from your data.
Meanwhile, on the machine-learning side of Big Data, Amazon Personalize enables companies to deploy targeted marketing campaigns, as well as personalized product and content recommendations. The tool is useful for encouraging customer engagement and loyalty, which increases long-term revenue potential.
Overall, these AWS features and more can help you reduce ongoing operating expenses, identify new sources of value, and increase long-term ROI.
Increase Productivity and Performance
Big Data solutions shouldn’t only increase how much information a company can handle. Modern analytics should benefit the organization in multiple ways, from increasing productivity to improving development pipelines.
With machine learning and AI programs, companies can forecast outcomes and build recommendation engines that cater to individual customers. Organizations can keep up with market disruption and launch products that capitalize on recent trends. Big Data can also boost innovation by allowing development teams to test and deploy offerings quickly.
AWS tools can boost Big Data performance in numerous ways. For example, Amazon Redshift users can efficiently query data from cloud applications, smart devices, and IoT sensors to extract deep insights quickly. The solution also allows you to easily push data to other analytical tools and execute queries without ETL pipelines.
For those who want to query data in open file formats, AWS offers several tools specifically for data lake and data warehouse needs. This is the lake house concept. It involves allowing Amazon S3 and Amazon Redshift to interact and share data so that users can have the advantages of each product. Users no longer have to load all the data into data warehouses for processing or analysis. Only the subset (“warm” data), frequently used in analytics, is continuously kept in Redshift, while everything else (“cold” data) is reliably and cost-efficiently stored in the lake.
On the AWS Big Data blog, you can find information about optimal design patterns for lake houses and other strategies for maximizing spending impact related to data warehousing.
When implementing Big Data analytics, you should think about what other parts of the business you can enhance. Implementations driven by best practices will increase not only analytical potential but also impact essential operations in many ways.
Handle Massive Volumes of Data
One of the most attractive promises of Big Data is its ability to uncover meaningful insights from massive data volumes. Modern analytical applications can gather intel from remote IoT sensors, mobile devices, and software applications. The best solutions can automatically process this data for visualization or further analysis without any manual intervention.
However, not all Big Data solutions are created equal. Managing massive amounts of information across the entire data lifecycle is challenging without the right set of features. Following best practices gives you virtually unlimited scalability when it comes to extracting, transforming, loading, storing, and processing data.
Your team shouldn’t have to spend time or energy on any of these activities. Make sure your solution can automate these tasks with near-perfect accuracy without burdening the enterprise.
AWS facilitates scalability through its robust set of serverless technologies. Amazon S3 is a highly durable tool that can gather and store any amount of data from anywhere. Using Amazon Athena, businesses can query data stored in S3 without having to manage any infrastructure. Athena is also a pay-as-you-go service, which means you only incur costs for queries you run.
On the data lake front, AWS offers Lake Formation, a service that simplifies data lake setup. As mentioned previously, AWS Glue is a serverless ETL service that manages provisioning, configuration, and scaling on behalf of users. And with Amazon Redshift’s new RA3 nodes, companies can scale storage and clusters according to their computing needs.
Trusted Data at Every Stage
Another important consideration is the process of moving data between systems. That could include copying data, reformatting it, combining multiple data sources, or many other steps. Each step might require separate software. That’s where data pipelines come in.
A data pipeline provides a smooth, automated flow of data from one point to the next. It determines what, where, and how data is collected. Data pipelines then automate the processes involved in extracting, transforming, combining, validating and loading data for further analysis. That helps eliminate errors and combat bottlenecks or latency.
AWS Step Functions, a general-purpose workflow management tool, is critical for executing data pipeline orchestration. It works by coordinating multiple AWS services into serverless workflows. Other key services for effective data pipelines include Amazon Kinesis for collecting, processing, and analyzing real-time, streaming data at scale, and AWS Glue for querying and processing data.
Achieve Big Data Best Practices with ClearScale
At ClearScale, we’ve helped hundreds of companies optimize IT infrastructure to take advantage of AWS Big Data tools. Our team understands how to implement modern analytical techniques so that businesses can create value, minimize risk, and innovate:
● We recently helped a financial advisory firm, Spartan Capital Intelligence, build a software application that sources data from all over the web to help platform users make sound investment decisions.
● We orchestrated a new data pipeline with a scalable data lake to provide faster data processing and more granular analytics for marketing technology company Conserve With Us.
● We also enabled a biotechnology company, microTERRA, to build out an IoT infrastructure so that customers could track water quality in real-time across vast geographies.