We were growing very fast as a startup and needed a way to accelerate our time to value for Hadoop,” explains Mickey Alon, Insightera’s CEO and Co-founder. “We wanted to focus more on data processing and turning insights into actionable results, and less on the operational side of Hadoop and Amazon S3 for tackling our Big Data integration challenges.
Insightera’s CEO and Co-founder
Insightera is the first learning B2B targeting and personalization platform. Insightera helps marketers capitalize on their existing assets by personalizing onsite, social and ad network experience based on real-time discovery of prospects’ industry, organization, location and digital journey. By using machine-learning algorithms, Insightera continuously improves ROI, auto-tunes campaigns and adjusts content accordingly for both known and anonymous prospects. Insightera’s software works with any CMS, using any content and requires zero IT. Insightera was founded in 2009 and is headquartered out of San Mateo, California.
Insightera’s cutting edge, inbound B2B marketing platform leverages Big Data, machine learning and predictive analytics to display the most relevant content to targeted high-yield prospects in real-time. Insightera selected Hadoop as its marketing platform’s Big Data foundation, but found it to be complex and time consuming from an operational perspective.
“We were growing very fast as a startup and needed a way to accelerate our time to value for Hadoop,” explains Mickey Alon, Insightera’s CEO and Co-founder. “We wanted to focus more on data processing and turning insights into actionable results, and less on the operational side of Hadoop and Amazon S3 for tackling our Big Data integration challenges.”
Insightera’s forte is turning insights into action using its proprietary real-time CEP engine powered by predictive analytics. Its data scientists wanted to find a way to simplify Big Data operations so they could test out and release key predictive algorithms that later on turned into one of Insightera’s core capabilities. Rather than hiring additional Hadoop experts who can be expensive and hard to find, Insightera decided that it would be more advantageous to use a Big Data as a Service solution.
When evaluating Big Data as a Service solutions, Insightera did its homework. According to Alon, “We looked at similar offerings, but quickly discovered that Qubole offered the most mature solution. The final decision boiled down to choosing between Qubole Data Service (QDS) and Amazon EMR. We selected QDS because it provided a better user experience, auto-scaling, more flexible cloud integration and a wider selection of training resources.”
With regards to the user experience, Insightera found that QDS provided the most intuitive tools and the highest degree of automation. Its query editor and visual query builder offered Insightera’s developers and data scientists an easy way to access Hadoop data with no specialized MapReduce and Pig coding skills. Because QDS runs on an elastic Hadoop-based cluster, Insightera could automate cluster configuration and management so that its small Big Data team wouldn’t have to spend their time on these activities.
Insightera valued QDS resource utilization features, including Amazon S3 I/O optimization, faster queries and self-managed auto-scaling to scale capacity up and down as needed without having to manually reconfigure resources. Insightera viewed auto-scaling as essential not only to meet unanticipated demand from big brands in its customer portfolio, but also saw auto-scaling as a way to save on cloud compute costs.
In addition, QDS received high marks from Insightera for its cloud integration. QDS provided connectors to its MongoDB and MySQL sources for Insightera’s behavioral data, offered a built-in Hive User Interface, and fast I/O performance for Amazon S3. Plus, QDS made it easy to marry behavioral data with reference data such as social media activity and IP address lookups.
Insightera was able to get QDS up and running in just a few weeks. As Insightera’s business grew 200 percent in just one year, its data volumes grew at the same rate, reaching over 3 terabytes. With the help of QDS, the company avoided time-consuming, complex and expensive administration typically associated with this massive amount of Big Data, realizing several benefits:
Most importantly, QDS empowered Insightera’s team with a Big Data platform that requires zero-IT and that resulted in faster time-to-market. QDS made configuring and managing Hadoop clusters, adding data sources, and running queries very simple. Alon comments, “My team learned how to perform data loading and run queries with QDS in just a few minutes. They became masters in a couple of days.”
Insightera is currently focusing on increasing capacity to support its ever growing volumes of Big Data. To accomplish this, Insightera will be taking greater advantage of the fully automated clustering capabilities offered by QDS. This includes using its scheduler and workflows for data loading, storage and updates as well as leveraging its cluster API.
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.”