Apache Spark Solutions
Apache Spark is a fast and general engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Apache Spark™ is a fast and general engine for large-scale data processing. Spark has an advanced DAG execution engine that supports cyclic data flow and in-memory computing. Spark has over 80 high-level operators that make it easy to build parallel apps, and you can use it interactively from Scala and Python shells. Spark powers a stack of high-level tools including Spark SQL, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3. You can run Spark readily using its standalone cluster mode, on EC2, or run it on Hadoop YARN or Apache Mesos. It can read from HDFS, HBase and S3, Cassandra, and any Hadoop data source.
Use Cases for Apache Spark
Runs programs up to 100 times faster than Hadoop MapReduce in memory, or 10 times faster on disk.
Writes applications quickly in Java, Scala or Python.
Combines SQL, streaming, and complex analytics.
Used by one location technology company to enable brands to reach on-the-go consumers.
Builds predictive models and recommendation systems for marketing automation and personalisation.
Builds large scale analytics platforms for telecoms operator.
Key Benefits of Apache Spark
- Fast and easy to use.
- Good generality.
- Runs everywhere.
- Provides faster, more meaningful insights and actionable data to operators.
- Visual, Real-Time, Predictive Analytics.
- Provides a cost effective data centre solution.
- Allows the interactive exploration of large datasets.
- Analyses usage patterns.
Features of Apache Spark
- Ease of Use.
- Runs Everywhere.
- Open-source software under Apache Licence.
Our Apache spark consulting services
- Software Lifecycle Management / Software Development Life Cycle (SDLC).
- AWS Cloud Hosting.