Rtech gives you Big Data results, quickly and easily.
Data at your service.
The Rtech Data Cloud is secure, scalable, and comprehensive. And our operations team ensures it’s hassle free.
The Rtech Data Cloud was built to solve enterprise-class Big Data challenges with ease. It is a comprehensive Big Data solution, based on the Apache Hadoop and Apache Spark ecosystem. It offers a full data science workbench. It is surrounded with world-class security technology and process protections. And it all runs in a highly secure datacenter where the hardware and networking are specifically selected, configured, and managed for Big Data operations.
The result is elasticity, high reliability, and blazingly fast performance. Your Big Data jobs get done—quickly—without unnecessary stress. That’s why some of the largest organizations in the world trust Rtech with their data and analytics and even run their own applications on top of Rtech for their customers.
Rtech Data Cloud
The Rtech Data Cloud is specifically constructed and managed to deliver high-performance Big Data results. A comprehensive solution built to satisfy the demands of a broad range of data analytics requirements, it comes preconfigured with core compute engines such as Spark, Tez, and MapReduce, as well as services such as Hive, Oozie, Pig, and Spark SQL.
Rtech runs Hadoop on hardware that is purpose-built and tuned for Hadoop. Not only does Rtech select the best hardware for Big Data, we also configure the kernel and network parameters on top of the hardware for Big Data performance.
As a result, the Rtech Data Cloud handily beats the performance of its competitors, as proven in real-world examples by Rtech customers.
Storage Services - HDFS
The Rtech operations team works around the clock for you, monitoring the clusters for hardware or software failures. We take professional pride in ensuring that your service is up to date, fault tolerant, and reliable.
Rtech supports Kerberos-enabled Hadoop clusters, which ensure the user is authenticated before accessing HDFS.
Rtech considers scale-out architecture to be core to its business. Customers can easily expand capacity as their needs increase without having to worry themselves about hardware. Rtech customers benefit from elastic storage, which grows and shrinks to meet customer needs over time.
Storage services primarily consists of Hadoop Distributed File System (HDFS) and HCatalog. HDFS is a highly reliable, fault-tolerant, distributed storage system used for storing and retrieving Big Data at high throughput. HCatalog is a storage management layer that provides a common interface to multiple compute services.
Ready for Any Job, Anytime
Run simultaneous applications on the same dataset.
Utilize Altiscale’s elasticity and expand service capacity to accommodate spiking demand.
Rtech is optimized for Big Data and easily scales as overall data volumes grow.
Resource management at Rtech Data Cloud is managed through YARN (Yet Another Resource Negotiator), a large-scale distributed operating system for Big Data introduced in Hadoop 2.x, which improves significantly on resource management in Hadoop 1.x. Big Data jobs often come in bursts, requiring rapid shifts in processing capacity. While some jobs might only require a small amount of computing capacity, others might dramatically exceed the data volume of an average job. One of the key benefits of the Rtech Data Cloud is elasticity—giving customers rapid access to the capacity they need to get their largest jobs done, without having to worry about hardware or job scheduling. Since Rtech is in the cloud, the resources for scaling are simply available whenever the customer needs them.
YARN lets multiple data processing engines, such as Spark, Tez, and MapReduce, run on top of Hadoop. This unlocks an entirely new approach to data analytics by enabling multiple analytics frameworks to run simultaneously and take advantage of the data stored in HDFS.
Resource Management - YARNN
Compute services consists of a set of engines and basic services that sit on top of the compute engines to perform different types of processing. There are several compute engines that run on top of Rtech. Depending on the use case, any of the following can be used on top of Rtech Data Cloud.
Apache Spark Run Spark in production.
Apache Tez Run your existing MapReduce jobs faster on Tez.
MapReduce Use the most stable, batch-based processing engine.
Apache Hive Use the most reliable SQL-like data warehouse purpose built on Hadoop. Visualize using Tableau or any tool that connects to Hive using JDBC or ODBC.
Superior Architecture drives superior results.
Road what Forrester Consulting has to say about the benefits Rtech customers experience.
Forrester Report: “The Total Economic Impact of Rtech”
HADOOP JOB FAILURES
JOB COMPLETION TIMES
SIGNIFICANT IMPROVMENT IN
DATA SCIENTIST PRODUCTIVITY
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