Apache Spark Consulting, Implementation, Support and Fine-tuning
Apache Spark services help build Spark-based big data solutions to process and analyze vast data volumes. Since 2013, ScienceSoft renders big data consulting services to deliver big data analytics solutions based on Spark and other technologies – Apache Hadoop, Apache Hive, and Apache Cassandra.
Spark Use Cases We Cover
Streaming data processing
Apache Spark enables companies to process and analyze streaming data that can come from multiple data sources, such as sensors, web and mobile apps. As a result, companies can explore both real-time and historical data, which can help them identify business opportunities, detect threats, fight fraud, foster preventive maintenance and perform other relevant tasks to manage their business.
Interactive analytics
Interactive analytics gives the ability to run ad-hoc queries across data stored at thousands of nodes and quickly return analysis results. Thanks to its in-memory computation, Apache Spark is a good fit for this task. It makes the process time-efficient and enables business users to get answers to their questions, if they don’t find them in standard reports and dashboards.
Batch processing
If you are not a complete stranger to the big data world, you’ll say that it’s Hadoop MapReduce that is perfect for batch processing. But don’t fall for it: Apache Spark can do it too. And compared to Hadoop MapReduce, Spark can return processing results much faster. However, this benefit comes with the challenge of a high memory consumption, so you’ll have to be careful and configure Spark correctly to avoid piling up jobs in a waiting status.
Machine learning
Apache Spark is a good fit, if you need to build a model that represents a typical pattern hidden in the data and quickly compare all newly-supplied data against it. This is, for example, what ecommerce retailers need, if they want to implement the you-may-also-like feature on their website. While banks need to detect fraudulent activities in the pool of normal ones.
Apache Spark can run repeated queries on big data sets, which enables a machine learning algorithm to work fast. Besides, Apache Spark has an in-built machine learning library – MLlib – that enables classification, regression, clustering, collaborative filtering and other useful capabilities.
Cooperation Models We Offer
Memory issues
In-memory processing is Spark’s distinctive feature and an absolute advantage over other data processing frameworks. However, it requires a well-thought Spark configuration to work properly. One of the multiple things that our developers can do is indicate whether RDD partitions should be stored in memory only or also on disk, which will help your solution function more efficiently.
Delayed IoT data streams
IoT data streams can bring challenges, too. For example, the number of streaming records grows, and Apache Spark is unable to process them. As a result, a queue of tasks is created, IoT data is delayed and memory consumption grows. Our consultants will help you avoid this by estimating the flow of streaming IoT data, calculating the cluster size, configuring Spark and setting the required level of parallelism and the number of executors.
Troubles of tuning Spark SQL
Tuning Spark SQL performance can sometimes be necessary to get the required speed of data processing and can pose some difficulties. Our developers will take care of what file formats should be used for operations by default, set the compression rate for caching tables, as well as determine the number of partitions involved in the shuffle.
All about Data Analytics and Big Data
Data Analytics Services
Data Warehousing
Data Science
Big Data Services
Solutions
Business Intelligence
Data Management
Microsoft Business Intelligence