With 5 years of experience in data analytics and 5 years in big data consulting, we know how to deliver a Spark-based analytics solution tailored to your needs. Our consultants are ready to support you at any stage of your big data journey, efficiently tackling the challenges you may encounter on the way.
Qrapp's expertise covers a wide range of big data technologies, such as Apache Hadoop, Apache Hive and Apache Cassandra, but among big data processing frameworks, Apache Spark is the one we cherish the most.
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 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.
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.
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.
Our consultants bring in their deep knowledge of Apache Spark, as well as their hands-on experience with the framework to help you define your big data strategy. You can count on us when you need to:
With our consultants, you’ll be able to better understand Apache Spark’s role within your data analytics architecture and find ways to get the most out of it. We’ll share our Spark expertise and bring in valuable ideas, for example:
Are you planning to adopt batch, streaming or real-time analytics? Process cold or hot data? Apache Spark can satisfy any of your analytical needs, while Qrapp can develop your robust Spark-based solution. For example, our consultants will advise which data store to choose to achieve expected Spark performance, as well as integrate Apache Spark with other architectural components to ensure its smooth functioning.
Apache Spark is famous for its in-memory computations, and this area is the first candidate for improvement, as the memory is limited. You don’t get the anticipated lightning-speed computation and lots of your jobs are in the waiting status, while you are waiting for analysis results? This is disappointing, yet fixable.
One of the reasons can be a wrong configuration of Spark that makes a task require more CPU or memory than available. Our practitioners can review your existing Spark application, check workloads and drill down into task execution details to identify such configuration flaws and remove bottlenecks that slow down the computation.
No matter what problem you experience – memory leaks due to ineffective algorithms, performance or data locality issues or something else – we’ll get your Spark application back on the rails.
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.
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
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.
If you experience any troubles with your existing Spark-based solution or planning to implement one from scratch, our Spark consultants will be glad to share professional advice and help you at any stage of your project. To start cooperation with them, you only need to drop us a line and get a free consultation.