Implement Spark for predictive analysis to find new business opportunities
Softweb Solutions’ complete and integrated services can address all the big data-related requirements of enterprises. We develop data-centric apps to help businesses implement Spark in their organizations for making predictive analysis and gaining actionable insights.
Faster Application Development
When it comes to big data processing, Spark is way faster than a number of similar tools including Hadoop. The tool utilizes in-memory computing and works at a lightning speed even when the data is saved on-disk.
Easy to Use
Besides comprising APIs to process large datasets, this tool features Azure HDInsight and can be implemented in the Hadoop ecosystem. Developers can create applications for Spark using Java, Scala and Python.
Spark Core Engine
The tool features Spark SQL to run SQL-like queries with BI tools and visualizations, Spark Streaming for processing real-time streaming data, Spark MLlib for common learning algorithms and GraphX computation engine.
Compatible with Multiple Tools
Spark is complaint with various types of tools. You can run it on open-source frameworks like Hadoop and Mesos, in the cloud or as a standalone tool. Also, it can access data from Cassandra, HBase, HDFS, R and S3 with utmost ease.
- Spark MLlib
- Spark GraphX
- Spark Streaming
This scalable machine learning library features algorithms that include classification, regression, dimensionality reduction, clustering, optimization and collaborative filtering. This module also lets you enjoy the benefits of relational processing like optimized storage and declarative queries.
This API for graphs and graph-parallel computation includes a collection of algorithms and builders that simplify graph analytics. It is a specialized graph processing system meant to help you improve your business performance.
Spark Streaming makes enterprise apps capable of processing data at a fast rate and receiving new data in real time. Based on micro batch style of computing and processing, it enables interactive and analytical applications across historical and streaming data with its fault tolerance characteristics.
Why organizations must implement machine learning
- Machine learning in general
- Why it is relevant more than ever
- Supervised Learning – features, algorithms, applications
- Unsupervised Learning – features, algorithms, applications
- Examples and strategies
Application of Apache Spark in various industries
Media and Entertainment
Spark can easily handle the millions of data points that media platforms generate and the tool’s machine learning algorithms can analyze them for useful outcomes.
Softweb Solutions can help you implement Spark to predict equipment failures, carry out preventive maintenance and analyze data collected by sensors installed in oil wells and other energy-generated infrastructure.
Spark can be useful for detecting money-laundering and other finance-related illegal activities. We help you understand your customer base and develop personalized recommendations for retention and revenue generation.