Apache Spark Development Solutions

It is a well-known fact that the world has an insatiable appetite for data. With the software industry growing rapidly, it is becoming increasingly challenging to manage large quantities of data.

Companies in several sectors — technology, eCommerce, retail, and social networking- struggle with their data management requirements. They want to manage, process and get meaningful information from their data. It is in this context that Apache Spark comes into play.

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Apache Spark is a unified analytics engine for large-scale data processing. And it has become a vital component of the big data stack for many companies. Apache Spark analytics solutions enable the execution of complex workloads by harnessing the power of multiple computers in a parallel and distributed fashion.

At our Apache Spark development company in India, we use it to solve a wide range of problems — from simple ETL (extract, transform, load) workflows to advanced streaming or machine learning applications. Contact our Apache Spark development experts to know more about how we can help you.

Apache Spark Analytics – Why Do You Need It?

Apache Spark is a general-purpose and lightning-fast cluster computing platform. It was developed to enhance the processing engine of Hadoop. The framework can run across various clusters managed by Hadoop YARN or Apache Mesos. There are many reasons why you need Apache Spark, like:

Ultra-Fast

Spark can take data from various sources — Cassandra, Hadoop, HDFS or S3, to name a few — and run programs up to 100 times faster than Hadoop MapReduce in memory or 10 times faster on disk. This speed is useful when handling fast-moving data that can't be processed fast enough with traditional analytics tools.

In-Memory Cluster Computing

Spark extends the MapReduce model to efficiently use it for more types of computations, which includes interactive queries and stream processing. The main feature of Spark is its in-memory cluster computing that increases the processing speed of an application.

Real-Time Data Streaming

When you need quick analysis of data streaming in from sensors (IOT), real-time recommendations based on live customer activity or the ability to process large amounts of data for machine learning models, Spark is an ideal solution.

Handle Any Data Challenge

Apache Spark is a powerful tool for big data problems. It's an engine for large-scale computation that can process large amounts of data in parallel across a distributed file system.

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Apache Spark Development Solutions

Apache Spark is a powerful platform that provides users with new ways to store and use big data. The technology has quickly gained steam among businesses using Apache Spark to find new revenue streams, increase efficiency, and respond to changing market demands in real-time.

At the same time, it's not enough to just be aware of the benefits of Apache Spark. To get the most out of your investment in the platform, you need a team that can help you leverage its powerful features. The team at Apache Spark development company in India has extensive experience assisting clients to develop Apache Spark solutions that address their unique challenges and objectives. We can help with various facets of Apache Spark development solutions like:

Data Ingestion and Management

Apache Spark provides a unified solution for managing and processing large data sets. We can process data from NoSQL databases like Cassandra, HBase, MongoDB, etc. and also from structured sources like relational databases. Further, we use it to ingest streaming data from sensors or machine logs in real-time.

Streaming Data Analytics

We use Apache Spark Streaming to build robust streaming analytics applications that combine batch processing with real-time stream processing over very large data sets. This is particularly useful in scenarios where you want to perform simple aggregations or complex analytics against data as it streams into your application.

Advanced Analytics Models

When you need to apply advanced analytics models against historical datasets, we can leverage the high processing power of Apache Spark to make predictions on the incoming streams. We perform these operations as you would on batch datasets.

Machine Learning with Spark

You can deploy various machine learning techniques over large-scale datasets using a scalable Apache Spark analytics solution. We leverage the power of machine learning algorithms combined with Spark's analytical and computational power to handle huge ML models.

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Our Expertise in Apache Spark Analytics Solutions

Apache Spark is an in-memory data processing engine built to enhance the speed and ease of data processing on Hadoop. It can perform multiple processing techniques, including batch, interactive, machine learning and streaming data. Since its launch in 2010, it has become one of the most popular open-source projects. So much so that many organizations have begun using it in production.

And at Aegis Softwares, we have been helping organizations in India and abroad with Apache Spark development and analytics solutions to reshape their approach to data and what it means.

Want to discuss your project? Shoot out a message to our Apache Spark development company now.

We use Spark for high-level APIs in Java, Scala, Python development services and R to support general execution graphs.
We also work with a rich set of higher-level tools, including Spark SQL for SQL and Data Frames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Our Apache Spark development experts are highly skilled in the distributed collections of items called Resilient Distributed Data sets (RDDs). We create RDDs from files or transform other RDDs, which can be operated on by multiple actions such as a map or filter on each item in the dataset in parallel.
Our team has expertise working with the libraries for SQL, machine learning, graphs processing and stream processing, making it easy to deploy at scale across private and public clouds.
We can process real-time streams effectively using DStreams – Discretized Streams. Streams are sequences of RDDs that are Spark's abstraction for distributed data sets or objects.
We also use Apache Spark development along with various other applications such as Hadoop, Apache Mesos, Kubernetes, etc. Spark and diverse data sources, including HDFS, Cassandra, HBase, S3 etc.
Our team also works with Apache Flink to compute an execution plan for distributed computations on data flows and perform both batch and stream processing.

Want to know more about our Apache Spark solutions? Reach out to our team today.