DOWNLOAD E-BOOK (LINK to MemSQL web site: registration required)

You’ll Learn:

• When to use in-memory database management systems
• Requirements and benefits of processing transactions and analytics in a single database
• Top architectures for transitioning from data silos to real-time data pipelines
• How to use Spark and in-memory databases to build real-time data pipelines
• Steps for getting to real-time operational systems
• Considerations for choosing the best deployment option

In the era of high-volume and high-velocity data, the faster you can process information and make decisions from it, the better. Accelerating data feedback loops gives you the agility to test many ideas, sort the good from the bad, and react to changes faster than the competition.

Getting to Real-Time

Building faster feedback loops requires ingesting large volumes of streaming data and also having tools to explore that data as it is captured. This is easier said than done, as legacy RDBMSs rely on high-latency batch loading, and NoSQL stores limit analytic functionality.

Transitioning to simultaneous data processing and analysis calls for adoption of a hybrid database model, where transactions and analytics converge in a single system. This means embracing evolving technologies like in-memory computing and distributed architectures that are optimized for rapid ingestion and exploration across large, changing datasets.

Free O’Reilly Ebook: Building Real-Time Data Pipelines

MemSQL teamed up with O’Reilly Media to bring you a complimentary ebook: Building Real-Time Data Pipelines – Unifying Applications and Analytics with In-Memory Architectures. This ebook will serve as your guide to achieving real-time business operations, providing examples of proven models, real-world use cases, and recommendations for implementation along the way.