Big Data Analytics on Hadoop
Analyze Multi-Structured Big Data on Hadoop At Lowest Operating Cost
Hadoop has become known as the platform of choice when you need to run analysis against large volumes of multi-structured data for better business insights.
Open source Apache Hadoop is ideal for low cost scale and enabling fast analytics against multi-structured data sets. Use-cases vary by sector and include customer behavior analytics for a retailer such as tracking web clickstream data or a telecommunications operator who can run predictive analytics in order to determine which customers are likely to churn and switch to a competitor. By tracking customer behavior patterns or preferences, you can perform targeted and personalized offerings, which ultimately improves customer satisfaction, loyalty and revenue.
RainStor’s Big Data Analytics on Hadoop product provides users a database that runs natively on the Hadoop Distributed Files System (HDFS) residing on the nodes. Unlike row or column databases, RainStor’s architectural compatibility with Hadoop enables you to run seamlessly together. RainStor stores data in simple large blocks or files, which is exactly how Hadoop is architected. Because of this, there is no need to move data in and out of the cluster and thereby you reduce the need for additional tools and resources costs.
Key Benefits include:
- Native on Hadoop and HDFS enables 50-80% Node Reduction
- Multi-Structured Data Management
- Faster Query & Analysis – 10-100X Improvement
- High performance Ad hoc Query – SQL
- MapReduce support Pig
- Enterprise Grade Security, Scalability, Resilience
RainStor Big Data Analytics on Hadoop capabilities include:
RainStor ingests raw, multi-structured network data handling 10′s of billions of daily records and as the data is ingested, it is automatically de-duplicated and compressed.
RainStor’s industry leading compression starts with value and pattern de-duplication resulting in zero redundancy of records. Further byte and algorithmic compression yields data reduction rates of 20-40:1.
RainStor supports both SQL and MapReduce, via Pig. Users can choose SQL for rapid response ad-hoc queries or run batch jobs using MapReduce against RainStor data. Additionally you can interoperate SQL and MapReduce and join results from a query against RainStor and against native CSV files on HDFS.
Hadoop is gaining in popularity because it is easy to scale as data volumes grow. RainStor is designed to scale up and out and you simply add additional nodes when capacity requirements dictate.
Because RainStor runs natively on Hadoop, users get deployments off the ground faster with standard SQL supported. There are no specialist DBA skills required to maintain RainStor.
RainStor uniquely provides all the enterprise standards IT has come to expect when running analytics platforms. RainStor has built in geo-replication, security and automatic recovery of partitions. Additional support for workload segregation enables fast and resilient deployments.
