Qubole gave us the usability and scalability we need to drive insights from adoption of big data across our entire enterprise.
Chief Technology Officer
TubeMogul’s software enables brands and advertisers to buy video advertising in real-time across multiple devices and formats using one comprehensive platform. The company’s video advertising business employs Big Data analysis from ad serving, targeting, optimization and brand lift measurement. While TubeMogul launched its own fixed cluster while at the same time running on Amazon EMR, this consumed valuable engineering resources.
One of the biggest priorities at TubeMogul with Big Data was to make data easily accessible to non-engineering groups. Big Data was in high demand across the whole organization. Marketing sought insights to educate customers and establish insights around best practices and trends. Marketing research analysts needed to understand trends in viewability, inventory available, price and much more. Business Development needed Big Data to work more effectively with publishers and data partners. The business intelligence team demanded better visibility into business metrics, brand safety and inventory analytics. The machine-learning team sought to make TubeMogul’s advertising more relevant by optimizing the process of presenting the right ad to the right person at the right time.
TubeMogul’s fixed cluster and Amazon EMR required significant engineering time to accommodate all of its business users. The company needed engineering involvement to setup and manage clusters, to determine which nodes to run, to access keys and to complete account management tasks. Adam Rose, TubeMogul’s CTO explains, “We had very large datasets and had to run queries every day to populate smaller normalized tables so that we could analyze data over time, for instance, historical price trends for the past six months. This required writing Python scripts, which can be time-consuming for our business analysts.”
When the company heard about Qubole Data Service (QDS), they jumped at the opportunity for zero cost of entry, no upfront capital expenses and no long-term commitment. Specifically, QDS’ auto-scaling, automated cluster management and the web-based tools that allowed its business users to efficiently schedule queries were particularly appealing.
Through QDS, TubeMogul’s data operations easily reached enterprise scale. This remained the case even as the company watched its data volumes swell more than 15 terabytes of log data daily.
QDS offered ease of use. QDS’s web-based console simplified the processes of setting up and scaling clusters and managing accounts. Analysts then used SQL and scheduled queries to be executed at pre-defined intervals instead of writing scripts or Python code. “This was the key to our successful enterprise deployment,” comments Adam Rose. “Making our business users self-sufficient got us where we wanted to be.”
QDS’ auto-scaling saves time and money. With this feature, TubeMogul can scale up to meet the demands of queries against large data sets when as many as 30 users are running queries simultaneously. The company no longer has to provision resources to guarantee consistent capacity.
After deploying QDS, TubeMogul enjoyed the following benefits:
Qubole’s high levels of responsiveness to requests for technical support and feature enhancements further assisted TubeMogul’s successful transition into a fully enterprise-scale operation.
Now that TubeMogul has successfully rolled out QDS, the company is focused on deriving even more insights through faster queries. TubeMogul will also use Presto-as-a-Service, a SQL engine that QDS has integrated to perform real-time interactive queries.
Qubole is a significantly more polished product than EMR. Data scientists can explore their data in S3, create tables and query those tables all via an easy-to-use web UI
Qubole’s fantastic support has been key in our successful deployment. They continue to deliver of new features and revisit the ones that we ask for
Our goal at MediaMath was to take our existing industry leading infrastructure to the next level handling new complex analytics tasks. Qubole has helped us enable this goal with minimal risk.
Instead of worrying about provisioning clusters of machines or job flows or whatever, Qubole lets you focus on your data and your queries … The Qubole guys have been extremely helpful!
The service spins up users’ clusters only when a job is started, then automatically scales or contracts them based on the workload, and spins the servers down once the job is done.
Qubole’s Hadoop and Hive interfaces are vastly superior to the default CLIs, which scare business analysts and hinder meaningful analyses of the gaming logs that we collect. With Qubole, business analysts are self-sufficient in using a Big Data platform to meet their advanced analytic needs.
Online Gaming Company
top-performing technologies in the data industry are definitely taking aim at democratizing data tools and bringing the power of data to smaller businesses. This is a major change in the data industry, and Qubole Data Service is a great example
I’m very happy to be using Qubole in production. Qubole has saved me a lot of time, effort, and trouble in getting my data processing pipelines up and running. My data pipelines process Appnexus data in Amazon S3 which is then stored in Vertica. The engineering team understands the complexities and provided awesome support!
Real-time Ads Retargeting Startup
There’s a whole world of web companies, SMBs and other non-Facebooks or Yahoos that will want to use Hadoop but not want to run it in-house…offering a cloud service makes it easier for these users to get started with the platform and for Qubole to keep improving.
Qubole offers a big data ETL and exploration service through auto-scaling Hadoop clusters with a web user interface for data exploration and integration with various data sources. The service can do (nearly) everything EMR can do, and it goes further
Big Data Republic
Simba knows Big Data access. Qubole knows Big Data. Qubole’s founders authored Apache Hive, built key parts of the Hadoop eco-system and brought Apache HBase to Facebook
“The integration of Tableau and Qubole makes it faster and easier for our customers to operationalize Big Data…lowers the resource barriers to deriving the benefits of Big Data because customers can deploy our joint solution seamlessly and cost effectively.”