So, you got Spark. Since you wanted to save tons on storage (plus eliminate all that hassle setting up and maintaining your architecture), you opted for Spark in the cloud. Check and check — you’re headed in the right direction. Now it’s time to make sure that you’re getting the most out of Spark in the cloud. No matter what your primary purpose for the setup was, big data has tremendous potential and way too many use cases to be locked up and reserved for a singular purpose in the organization. Instead of just using it for BI or for customer relationship management, think bigger. Spark can handle it, and in the cloud, so can you.
Data visualization is just a really uppity way of saying that you make the results of your analysis visual.
Think charts, graphs, infographs … Non-techies love it.
1. Data Visualization
In order for the entire organization to benefit from your analytical insights, it’s important to be able to make the data visual. There are numerous tools out there to help you with data visualization, and mastering one assures that you can prove your big data project’s value to everyone from the C-suite to your partners on down the organizational ladder.
2. Machine Learning
Machine learning isn’t just for the mad scientists at Google. Everyday companies can leverage ML for everything from improving cyber security to learning how to outdo the competition in terms of foresight and market insight. Granted, it takes a lot of data (varied data, usually from both inside and outside your organization) and some powerful algorithms to do ML right, but its potential is breathtaking.
3. Streaming ETL
One of the problems with data storage is that data often isn’t cleansed and aggregated properly before making its way to the data store. That means that your data isn’t clean, and any analytics you conduct aren’t accurate. Enter streaming ETL. Use Spark to cleanse and aggregate the data before storing it.
4. Data Enrichment
Sometimes the streaming data isn’t all you need to conduct thorough analytics (such as with the case of ML or AI). Spark can be used for data enrichment, the process of analyzing live streaming data with static data (such as historical data on your customers, inventory, or some outside data) for a more comprehensive analysis.
5. Event Detection
Spark is making enormous waves in the various fields of security, including fraud detection, cyber security, identity theft, and others. For example, it’s really good at detecting anomalies in Web traffic that might indicate a breach such as an ensuing DDoS attack. It’s also ideal for detecting fraud, including credit card fraud and insurance fraud. Hospitals use a similar technique for detecting patterns of patient vital signs that can indicate an impending turn for the worse. There are tons of use cases for Spark in event detection, depending on the industry you’re in. Manufacturing, retail, finance, healthcare, the media logistics … every sector has potential uses for Spark for event detection.
6. The IoT
Most industries are also delving into the IoT. Have you begun implementing IoT devices around your workplace? Spark makes the ideal analytical tool to manage the vast and rapid data streams inherent in the IoT. Whether you’re simply capturing customer data from your smartphone app or tracking GPS data from a fleet of commercial vehicles, Spark is the tool of choice when it comes to IoT analytics.
There is certainly a use case or ten in your organization for Spark. Have you found the right cloud partner to make it happen? Visit Bigstep today to see our products and how our architecture is right for you.