Today, we have an incredible amount of data at our disposal.
Billions of smart devices continue to flood the market, increasing the volume of available information every day. At the same time, the velocity at which we can process data is improving rapidly with advances in computing technology. We also have access to a wider variety of data, enabling us to glean richer insights about our world.
Businesses that can take advantage of Big Data analytics have a distinct advantage over those that can’t. However, many leaders mistakenly believe that all data is useful data. In reality, the value of data diminishes over time. Those who want to implement predictive analytics or make time-critical, data-driven decisions must process information instantaneously.
Fortunately, cloud providers, like Amazon Web Services (AWS), offer powerful services and tools to help organizations process streaming events at scale. With these capabilities, businesses can uncover new data insights quickly, respond to market disruption, and outmaneuver their peers. They can use continuous streaming data to develop tailored offerings, build real-time applications, analyze IoT devices, and much more.
There is no better time to develop a sophisticated process streaming infrastructure. To learn how read on, and watch this educational webinar:
Why Real-time Data Streaming is Hard
Before diving into how the cloud makes real-time data streaming easy, we should discuss why it’s so difficult for companies to do this on their own.
Between gathering data from numerous sources, setting up data pipelines, configuring storage, and extending information to other applications, there is a lot to manage. Many companies don’t have the resources to establish and monitor the analytics ecosystem needed to ingest large volumes of diverse data.
Companies that want to collect data in real-time need highly available and durable infrastructure. IT leaders also have to integrate data streams with other critical systems, which often requires custom development. To scale, organizations have to set up new servers, hardware, and data centers, as well as hire staff to take on oversight responsibilities.
Overall, real-time data streaming requires lots of equipment, people, and money unless you have access to a public cloud provider with purpose-built tools and services.
How AWS Enables Stream Processing
Over the years, AWS has developed a suite of cloud-native technologies to simplify the process of setting up and managing a real-time data streaming ecosystem.
The platform’s tools are easy to use, highly available, and elastic. Businesses can quickly scale analytics infrastructure and extend their data to other applications for further processing.
Additionally, AWS offers pay-as-you-go pricing, which means you only pay for what you use. Rather than deploy significant capital upfront or under-fund your analytics capabilities, you can start with what you need immediately without overpaying for unused computing capacity.
Below, we highlight several AWS services that we often implement for our clients. We hope this gives you a sense of what you can achieve in the cloud.
Process Streaming Events with Amazon Kinesis
Amazon Kinesis is a fully managed service that allows users to run their streaming applications on automatic. Kinesis can collect, process, and analyze data in real-time from many types of sources, including video, audio, and IoT devices.
The service creates an abundance of opportunities for organizations, as they can upgrade from batch analytics to evaluate massive data volumes continuously at low latencies. Rather than wait for all data to arrive, businesses can respond instantly to new insights in seconds without managing any infrastructure. Today, savvy organizations use Kinesis to deploy security monitoring, feed content recommendation engines, study customer behaviors, implement monitoring, and more.
With Kinesis Data Analytics, you can also implement real-time processing using the open-source Apache Flink framework. To prepare real-time data streams for other applications, you can use Amazon Kinesis Data Firehouse, which automatically scales with your data.
Catalog Event Archives with AWS Glue
AWS Glue is another valuable managed service for extracting, transforming, and loading data for analysis. All you have to do is point AWS Glue to your data stores for it to catalog your information, identify data formats, and suggest schemas. AWS Glue also generates code automatically for data transformations and loading processes.
Additionally, AWS Glue integrates with a wide range of services, including Amazon Redshift, RDS, and S3. Again, you only pay for the resources you use.
Query Your Archives with Amazon Athena
Amazon Athena is a serverless query service that enables you to analyze data stored in Amazon S3 with standard SQL. Users can evaluate large datasets without having to rely on complex ETL jobs.
Amazon Athena works fast, letting you glean valuable insights in real-time. While the service works well for ad hoc analyses, it’s also capable of executing complex queries.
Processing Streaming Events at Scale with ClearScale
For those with limited knowledge of the cloud or AWS, look no further than ClearScale for expert consulting support. Our team has helped numerous organizations optimize IT infrastructure around real-time data streaming goals in the modern economy.
Recently, we helped a Mexico-based biotech company, microTERRA, implement real-time data streaming across its IoT footprint. We automated much of what the microTERRA team was previously doing manually and designed sophisticated BI dashboards so that their clients could easily evaluate key metrics. To learn more about our approach, read the case study here.
We also worked with the technology platform, Conserve With Us, to create an efficient data pipeline that used many streaming services, including Amazon Athena, AWS Glue, and Amazon Kinesis, for real-time collection, querying, and processing. Click here to read the full case study. You can also read more about ClearScale’s data and analytics services.