Spark.NET Consulting

  • Spark.NET aka .NET for Apache Spark is a free, open-source, and cross-platform big data analytics framework leveraging the .NET ecosystem.
  • Spark.NET is cross-platform and can run on Windows, Linux, macOS as well as major cloud platforms. It works seamlessly with Azure HDInsight and Azure Databricks for big data analytics, MMLSpark for machine learning, SQL Server for HDFS and other data analytics tools.
  • High level Spark APIs that cover different features of Spark including Spark SQL, DataFrames, Streaming and MLLib are exposed for .NET developers.
  • Our team consists of Microsoft Regional Director, Microsoft Most Valuable Professional and Azure Insiders. We work closely with the team that creates Spark.NET at Microsoft.

Over the years, Spark has seen great acceptance in the technology industry. When it comes to large scale data processing or big data analytics, Spark has gained attention due to its lightning fast processing speed, batch and stream data processing, support for a variety of data sources and easy to integrate with applications written in C#, Java, Scala, Python and R. In addition to big data processing, Spark also provides a bundle of libraries that can be used for machine learning. There are many advantages of using Spark and if you wish to learn more about its ecosystem, libraries and components and its advantages over Hadoop, check out this article explaining the differences.

This article will focus on the Microsoft stack and briefly explain the adaptation of Spark in SQL Server 2019, Azure based platform viz Azure HDInsight, Azure Databricks and machine learning library called MMLSpark. You may be surprised to hear that Spark is now available on the very famous .NET and .NET Core platform as well. Our CEO of Cazton - Chander Dhall who is an awarded Microsoft Most Valuable Professional, Google Developer Expert, was fortunate to be a part of the project where he had access to the source code before the release. Now let's go ahead and take a quick look at those amazing platforms mentioned above!

Spark.NET

So far Spark APIs have been exposed for applications written in Scala, Java, Python and R. But the great news is that Spark is now available for .NET developers as a free, open-source, and cross-platform big data analytics framework. We are happy to announce that we have been part of the Spark.NET project for a while and today Microsoft has open sourced it.

Spark.NET will be available as a Nuget package that not just runs on Windows, Linux, and macOS using .NET Core, or Windows using .NET Framework but also runs on major cloud platforms. High level Spark APIs that covers different features of Spark including Spark SQL, DataFrames, Streaming and MLLib are exposed for .NET developers. Applications written in C# or F# on .NET platform can easily integrate and leverage these features to create revolutionary Big Data solutions on premise or on cloud.

We are proud to announce that Cazton officially offers Spark.NET consulting, development, DevOps as well as training services now. Cazton team of Big Data experts are here to help you.

Azure HDInsight with Spark

With Azure's HDInsight, you can perform big data analytics and near real-time data processing on the cloud with Spark. Spark has been known for its fast in-memory data processing. With Azure HDInsight, you can create Spark clusters and develop solid big data solutions in the cloud. These Spark clusters provide support for various Spark based components like Spark Core, Spark SQL, Spark streaming APIs, GraphX, and MLlib. These clusters on HDInsight offer a fully managed Spark service and is fully compatible with Azure storage and Azure Data Lake Store. HDInsight with Spark can be a great resource for data analysts and business experts as it will help them analyze big data, develop reports as well as build real time analytics pipeline for their big data. Azure HDInsight with Spark is available as a PAAS (platform-as-a-service).

Azure Databricks

Azure Databricks is a Spark based analytics platform, which is optimized for Azure. It is a SAAS (software-as-a-service) that provides one-click setup-fully managed spark clusters, streamlined workflows, and an interactive workspace for easy collaboration between data scientists, data engineers and business analysts.

It has built-in integration with Azure Blob Storage, Azure Data Lake Storage, Azure SQL Data Warehouse, Cosmos DB, Azure Event Hub, Apache Kafka for HDInsight, and Power BI, which allows data engineers to transform static and streaming data.

Data scientists can leverage the same platform to create machine learning models from the transformed data. Whereas data analysts can create reports, graphs or charts through Power BI. With Azure Databricks, one can process huge amounts of data and develop, train and deploy models on that data, and manage the whole workflow process from one single place.

MMLSpark

MMLSpark stands for Microsoft Machine Learning library for Apache Spark, which is an open source library that has been built on top of Sparks machine learning library - SparkML. The idea behind developing this library was to address difficulties faced by Data Scientists in low level operations like support for image processing, text analytics and deep learning.

This library not only simplifies the process of data science but also seamlessly integrates SparkML pipelines with deep learning, Microsoft's Cognitive Services and OpenCV.

SQL Server 2019

Microsoft's SQL Server has long been the most preferred database platform for millions of developers, but with the release of SQL Server 2019, Microsoft has taken this platform to the next level by integrating Spark, Hadoop Distributed File System (HDFS) and data analytics tools directly into this new version of SQL Server.

Without having to move the data, you can now write a T-SQL query and manage data in and from various data sources like Excel Files, NoSQL and cloud databases like Cosmos DB and MongoDB, relational database like SQL Server, Oracle and Teradata or it can be any data lake from Cloudera or HortonWorks. You can also run advanced analytics and machine learning with Spark, use Spark streaming to data to SQL data pools, and use Azure Data Studio to run Query books that provide a notebook experience.

SQL Server 2019 also provides deployment of large data clusters in Kubernetes containers. Each cluster will provide support for SQL Server, HDFS and Spark enhancing its importance for production level deployments.

How can Cazton help you with Spark.NET Consulting?

With big data analytics, data science and AI, it's quintessential to have the right team and understand how to manage them. Some of the work our team has to do is change the mindset of using archaic processes for big data processing, data science and machine learning. Expertise, experience and our company's history of success is crucial in making each project successful. Delays in projects not only reduces the competitive edge of companies, but can also result in massive layoffs. We, at Cazton, work with you ensure you are successful both as an individual by rising higher in your career and as a company by staying innovative and ahead of the competition.