The Present – Invest in Equipment and Process
Until recently, Enterprises had to select platforms, estimate workloads, procure servers, and build out the infrastructure. Once the servers were finally in place, there is data ingestion, schema design, and Extract Load and Transform (ELT).
With procurement costs, approval cycles, and engineering ramp-up, you’re talking about significant investment in time and money – just to get to the starting line to finally analyze what value you can get from our data.
Beyond the cost of getting to that starting line, you need to keep your servers running. There is considerable skill required to keep a Hadoop cluster running, let alone secure and properly governed.
The Future – Big Data as a Service
Enter Santa Clara-based Qubole, and their cloud-based Qubole Data Service (QDS). With QDS, there’s no need to manage and support underlying Big Data infrastructure. With Qubole, you don’t have to put in months of planning and architecting your solutions. You create your account and move straight to working with your data. Their big data platform manages every aspect of analyzing big data in the cloud platform of your choice.
Qubole was founded by Ashish Thusoo and Joydeep Sen Sarma – who led the Data Infrastructure team at Facebook during which they also authored the Apache Hive project. Their mission was to design a solution for companies who need a fast, easy, and enterprise-scale solution for big data analyses, but didn’t have the necessary skills or bandwidth to deal with their own Big Data infrastructure.
With a QDS subscription, you also get the Qubole team, providing governance and 24/7 support, along with multi-petabyte scale infrastructure for your Big Data workloads. And you have many options to choose from when it comes to big data processing engines, including Hive, Spark, Presto, Hadoop, and HBase.
QDS is a self-service cloud-agnostic platform available over public clouds like Amazon, Oracle, Microsoft and Google. It allows your team to utilize its cloud-based BDaaS as needed without having to invest the time and resources to maintain expensive hardware.
Add to this QDS connectors for Redshift, MySQL, Oracle, PostgreSQL, Vertica, and MongoDB as well as ODBC and JDBC drivers, and you have a lot of choices for how you want to ingest, export, access, process and visualize your big data in the cloud.
Self-Service Data Analysis for Your Entire Team
Data Analysts, Data Scientists, and ETL Engineers use Qubole to manage hundreds of clusters on their public cloud of choice and begin running ad hoc and/or batch queries in under five minutes. Even non-technical users who aren’t trained to develop their own queries can use smart query builder or execute pre-built and saved queries. With self service big data analytics in the cloud, what used to take weeks now takes only a few hours.
When it comes to data visualization, self service big data analytics allow you can choose from many tools, including Tableau, Birst, Qlik, Pentaho, Alteryx, and Power BI.
Getting Started with Qubole Self Service Big Data Analytics
You can get a lot of mileage out of the QDS, but many organizations will want additional help from from data engineering, systems integration, or data analytics consultants. Qubole’s model is to deliver professional services through partners such as DesignMind. Our Big Data team has worked with Qubole at data-intensive organizations such as MyFitnessPal / Under Armour Connected Fitness.
If you want to kick the tires of a Big Data system, an easy starting point is Qubole Data Service. If you get more ambitious, or just want help getting started, contact us, and we’ll help you achieve Big Data success.