Join our experts as they give you a definitive look at what exactly a CISO job role entails and some tips on becoming one yourself.
The move to AWS big data continues to gain momentum, and it’s easy to see why. As more and more human activity moves online, the need for big data analytics is becoming that much more critical — especially among organizations that haven’t yet recognized the urgency.
The reality today is that virtually every credible enterprise has data management challenges that are beyond the capacity of traditional databases. Those challenges can be summarized as the “three Vs”: volume, variety and velocity. In terms of volume, today’s data has exploded from mere gigabytes to terabytes, petabytes and beyond. The variety of data has expanded, too, with the rise of social media and e-commerce activity. Additionally, today’s data needs to be collected, stored, processed and analyzed in real time, meaning that it now moves with unprecedented velocity.
Unfortunately, not every organization recognizes the need to prioritize big data analytics, and the consequences of ignoring big data are significant — from reduced productivity and competitiveness to increased costs. By contrast, embracing big data analytics from esteemed providers like Amazon Web Services (AWS) can lead to operational and cost efficiencies, through both the realization of new opportunities and the migration of heavy workloads to big data technologies in the cloud.
So, what’s the value of using AWS big data services? It begins with AWS’s broad and fully integrated portfolio of cloud services. Together, these services make it easy to build, secure and deploy your big data applications. With AWS, you don’t have to worry about hardware or infrastructure, which means you have more freedom and ability to focus your resources on uncovering new business insights.
The 6 AWS Big Data Analytics Options Explained
AWS’s greatest contribution to big data comes in the form of its fully integrated suite of analytics options. These six services provide elegant solutions for automating data analysis, manipulating datasets and deriving insights. Together or individually, they put your organization on the path to even greater responsiveness, innovation and competitiveness.
1. Amazon Kinesis
With Amazon Kinesis, you can move from batch processing to effortlessly collecting, processing and analyzing video and data streams in real time. This scalable, fully managed tool is ideal for a wide range of use cases, including security monitoring, facial recognition, fraud detection, IoT, application monitoring, machine learning, live leaderboards and more.
2. Amazon EMR
This leading big data platform enables you to process massive amounts of data via open-source tools like Apache Spark, Apache Hudi, Apache Flink, Apache HBase, Apache Hive and Presto. Amazon EMR automates time-consuming tasks, such as provisioning capacity and tuning clusters, making it easier to set up, run and scale your big data environments. Plus, EMR enables you to run petabyte-scale analysis at less than half the cost of traditional on-premises solutions.
3. AWS Glue
AWS Glue is serverless integration that simplifies the discovery, preparation and combining of data. Glue automates much of the work involved in data integration, including the crawling of data sources, the identification of data formats and the provision of schemas for data storage. In addition, Glue allows multiple groups to collaborate on data integration tasks, meaning that the data you need for analytics, machine learning and application development is available to you in minutes instead of months.
4. Amazon Machine Learning
Amazon Machine Learning (Amazon ML) is the robust cloud-based service that delivers machine-learning technology to developers at any skill level. Amazon ML’s visualization tools and wizards guide the user step by step, eliminating the need to learn complex ML algorithms and technology. Once your ML models are ready, Amazon ML offers predictions for your application — with no need for infrastructure or custom prediction-generation code.
5. Amazon Redshift
Whether you’re managing just a few hundred gigabytes of data or more than a petabyte, Amazon Redshift offers a warehouse service that’s optimized for your business intelligence needs. Redshift plays nicely with the familiar best practices in SQL, which helps to simplify ramp-up and adoption. And, compared to traditional data warehouses, Amazon Redshift is remarkably affordable, as it blends both entry-level pricing and massive cost efficiency at scale.
6. Amazon QuickSight
This serverless, cloud-native business intelligence (BI) tool generates interactive dashboards, complete with ML-powered insights. Amazon QuickSight enables you to automatically scale to tens of thousands of users without any need for infrastructure. Additionally, Amazon QuickSight is the first BI service with pay-per-session pricing, making your business insights more cost-effective to access.
Ready to Capture the Full Potential of AWS Big Data?
Making the most of AWS big data starts with getting the AWS training courses you need from ExitCertified. As an award-winning authorized AWS training partner, ExitCertified offers classes and certifications in all AWS technologies, including the top six AWS big data analytics options. Start exploring your AWS big data learning path and your AWS big data specialist certification today.
ExitCertified is a leading worldwide provider of award-winning, vendor-approved IT training. Since 2001, we’ve been helping our customers build the skills they need to thrive in an era of non-stop digital transformation. Today, we deliver more than 9,500 authorized courses for more than two dozen brands.