Apache Spark Financial Analytics has a distributed programming model based on an in-memory data abstraction called Resilient Distributed Datasets (RDDs).
Resilient Distributed Datasets (RDDs) are immutable, support coarse-grained transformations and keep track of which transformations have been applied to them. RDD immutability rules out a big set of potential problems due to the updates from multiple threads at once and lineages that can be used for RDD reconstruction.
Apache Spark Financial Analytics is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports the general execution graphs.
Apache Spark Financial Analytics also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.
Analyzing Financial Data with Apache Spark
With the rise of big data processing in the Enterprise world, it’s quite evident that Apache Spark Financial Analytics has become one of the most popular framework to process a large amount of the data to both in the batch mode and in real-time. Apache Spark provides fast in-memory processing at scale by the way abstracting the underlying data in the form of Resilient Distributed Data set (RDD).
Data set are analogous to distributed relational database and can be thought of RDDs with schema applied to the data. This provides the capability to query the data just like the relational database on top of the Apache Spark framework. The APIs for providing the SQL like construct on top of the RDDs is available via the Spark SQL library.
If banks want to achieve a proactive and intraday risk management while also effectively managing their capital over the long-term, they will require high-performing IT infrastructures that can handle much more intensive calculations.
However, many banks today rely on technologies such as relational databases and in-memory data grids (IMDG’s) to conduct risk analytics, aggregation and capital calculations.
IMDGs work by replicating data or logging updates across machines. This requires copying large amounts of data over the cluster network, making them expensive to run for analytics.
As a result, check pointing requirements are low in Apache Spark. This makes caching, sharing and replication easy. These are significant design wins, and there are other advantages over IMDGs too:
- Memory optimization
IMDG’s require the entire working set in memory only and are limited to the physical memory available. Apache Spark can spill to disk when portfolios do not fit into memory, making it far more scalable and resource efficient.
- Efficient joins
IMDG’s have fixed cubes and cannot do joins across data sets. Apache Spark supports joining of multiple data sets natively. This allows more flexible reporting without the need for new cubes and the additional memory. Joins are very performant in Spark.
- Polyglot analytics
Apache Spark supports custom aggregations and analytics which can be implemented in a variety of languages: Python, Scala, Java or R. IMDGs allow only limited SQL or OLAP expressions.
- Multi-tenant support
Apache Spark supports dynamic resource allocation, resource management, queues and quotas, allowing multiple users and processes to be supported on the same cluster. Some of these contain: operations reporting, decision support, what-if and back testing.
- Frugal hardware requirements
The immutable nature of RDD’s enables Spark to scale and provide fault tolerance efficiently. A Spark cluster is highly available without the need for the Active-Active hardware.
Locus IT remains committed to developing solutions in Apache Spark Financial services technology and provides services like Financial Services training, Financial Services support, Financial Services implementation and solutions to challenges that our customer’s face now and in the upcoming years. For more information please contact us.