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As the world moves to analytics at the speed of transactions, applications are required to process and analyze extremely large datasets instantaneously. Older generations of big data tools that took hours and days are increasingly outdated.
Geospatial data or location data is increasingly important for many applications, but epitomizes a complex real time big data challenge. Applications that use and manage geospatial data find it extremely challenging to manage high frequency read, writes and search requests at high volume.
Time series data is characterized by its sequential nature, frequency of collection and (often) high variability. Analysis of such data is often reduced to running calculations over summaries of data points to reduce processing overhead and extracting any kind of intelligence in real time is extremely difficult.
Spark, a general engine for large-scale data processing delivers significant advantages over using Hadoop MapReduce because of its cyclic data flow and use of in-memory computing. Redis with its blazing fast performance and optimized in-memory data structures reduces Spark processing time by up to 98%.
Caching is a technique frequently used to build highly responsive applications with an economy of resources. Effective caching requires a highly available caching layer that can scale economically, with top notch stable performance and low operational complexity.
High speed transactions are the mainstay of the financial industry, and are characterized by very high throughput and extremely low latency requirements. In addition to raw performance, transaction requirements include atomicity, consistency, isolation and durability.
