Apache Spark Implementation

Apache Spark is a fast and general-purpose engine for large-scale data processing. It offers up to 100x faster performance for specific applications thanks to an in-memory computational model that can operate on data stored either in memory or on disk.

At Aegis, we offer Apache Spark solutions to leverage its best capabilities to extend your computational power and gather more usable insights from your data. We can help set up a complete data pipeline from warehouse to analytics with our Apache Spark integration and deployment services.

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Here are a few things we can help with:

Development and production support of Spark applications
Development of custom RDDs, UDFs, and other low-level Spark components

Optimization of existing Spark codebases through refactoring and performance tuning
Implementation of Spark on YARN cluster for large scale data processing

Have a particular idea in mind? Contact our team to know more about our Apache Spark services.

How Do We Help with Apache Spark Implementation?

Apache Spark is an open-source big data framework for distributed processing introduced in 2010. It has since become one of the most used big data frameworks, with an active developer community. It provides high-level APIs in Java, Scala, Python and R and an optimized engine that supports general execution graphs. It also powers a stack of libraries, including SQL and Data Frames, MLlib for machine learning, GraphX, and Spark Streaming.

At Aegis, we leverage our deep expertise in the Apache Spark ecosystem to provide bespoke implementation services for our clients' big data projects. Here are the key services we offer:

Architecting Apache Spark Solutions

Our Spark consultants will work with your technical team to architect an Apache Spark solution that is completely optimized for your needs — both from a performance and a cost perspective. The Spark platform provides a wealth of options for designing data pipelines, so it's essential to consider all aspects of your infrastructure before making any decisions. Our experts will help you make the right choices from the start.

Implementing Apache Spark Applications

Once we've designed the ideal solution, our engineers will work with you to implement it on your infrastructure or within the cloud environment of your choice. We'll help install and configure Apache Spark clusters and then ensure that they're tuned for optimal performance.

Developing Custom Modules

Many organizations face highly specialized challenges when using Spark, particularly when developing analytical applications, where every project is different. Our development team can build custom modules that can be dropped into your existing Spark infrastructure or integrated into new solutions.

Implementing Data Pipelines

We can implement a production pipeline for your data so that it becomes available on-demand to all your teams. We can also work with you on data science projects, leveraging our expertise in machine learning, natural language processing and other advanced technologies to create custom solutions.

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Solutions

Our End-to-End Apache Spark Solutions

Our team has vast experience developing solutions using Apache Spark. Whether you need a simple connector or a complex, mission-critical application, we have the capabilities to deliver it. We don't just help with Apache Spark implementation, but provide a holistic solution starting from data ingestion to Apache Spark integration.

Data Ingestion

Our team is well-versed in using various data sources and formats, including file systems, NoSQL databases, RDBMSs and more. Plus, we specialize in using the latest Big Data technologies like YARN, Hive, HBase and others.

Data Processing Tuning & Optimization

We optimize big data pipelines for high performance so that you can execute complex queries across large datasets quickly and efficiently.

Real-Time Streaming Data Analytics

We use Spark Streaming along with related technologies like Flume, Kafka or Akka to enable real-time analytics over live streaming data such as IOT sensor data or log files.

Enterprise-Grade Security

Securing your data is our top priority — we use Apache Shiro for authentication and authorization, LDAP/AD for enterprise identity management and other tools to ensure that your data is safe from unauthorized access.

Machine Learning Algorithms

We develop custom machine learning algorithms using MLLib to help you get the most value out of your big data assets.

Apache Spark Integration

We integrate Spark with other big data platforms, storage and computational engines to maximize the efficiency of your overall operation.

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Use Cases of Apache Spark Services

Apache Spark is a very powerful framework that can be used to perform big data analytics. It has emerged as the future of data processing, with its ability to process real-time data at speeds exceeding that of Hadoop MapReduce. Apache Spark has single handedly changed the way organizations analyze and leverage the data for practical purposes.

One of the greatest strengths of this framework is that it offers data engineers an opportunity to develop applications that are faster than Hadoop MapReduce with more efficient use of resources. Although it was designed to improve performance compared to Hadoop, we've found some cases where the performance boost is tremendous. Here are a few use cases of our Apache Spark services:

Ingesting real-time telematics data from vehicles, aggregating it and providing it to other applications via a REST API
Creating derived tables in Hive using SparkSQL (we found this to be much more efficient than using HiveQL. The derived tables were used to create visualizations using tools like Tableau development services.
Processing many files stored on HDFS and generating reports based on the same.
Real-time ingestion of sensor data into HBase and Cassandra, then the aggregation of the same and making it available for real-time monitoring in UI dashboards.
Implementation of a data lake using Hadoop, Apache Spark and Hive on AWS EMR (Elastic Map Reduce) for an easy-to-use web UI for users to search for different events occurring in their log files.
Development of an Apache Spark Streaming solution based on Kafka messaging allowed the customer to have faster access to their data in real-time instead of waiting hours or days for the batch processing results.
Building a logistic regression model using R and implemented it with Apache Spark on HDFS for near real-time results.
Use Apache Spark implementation to significantly reduce the number of false positives and extend it to other fraud detection scenarios like illegal money transfers, credit card fraud, etc.

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