Big Data Integration with Apache Spark™
Hadoop => Most reliable,scalable and cost effective Big Data storage
Advise on what’s best for you
InfoObjects is your trusted partner in finding which Big Data solution works best for your needs. We are a vendor-neutral, client biased consulting company. Our strong focus is only your use-case and find which distribution works best.
We are a technology company doing consulting
We are a technology company at heart which happens to be doing consulting. This gives our clients an unfair advantage. They leverage our in-depth knowledge not only to find best solution for their needs but also build their IP.
Our commitment to open source
Open source technologies are a game changer in general and more so in case of Big Data world. We believe the kind of value open source software provides to clients is unparalleled. We are strongly committed to promoting, implementing and contributing to open source software.
We not only advise but also partner with you in implementation.
Implementation in Cloud
Cloud environments provide flexibility and agility which bring initial ramp-up time drastically. We help clients optimize Spark clusters on various cloud environments like AWS and Microsoft Azure. It includes various aspects like security, manageability and data governance.
For clusters of significant size, on-premise installation works out better than cloud. We help clients install and fine-tune Spark clusters in on-prem environments.
Our team of experts can help you process data using Spark and its libraries, so that you can derive actionable insights that improve your business.
Eureka or Enlightenment Phase
The promise of Big Data lies in being able to make more informed decisions – to increase sales, decrease costs or execute your mission more efficiently. Our Big Data analytics provide useful insights that until now could only be suggested by sampling or were completely invisible.
Visualize your way to insights
The insights you need are buried in huge amounts of fast-moving data in a variety of data types. Staring at raw data is not only often inefficient but can be also very boring. Humans believe in the power of stories; and the moment you start visualizing data, it starts telling stories.
We have expertise in all industry leading visualization tools like Tableau, Datameer and Qlikview. We can also help you create custom dashboards which provide tailor-made visualization interface.
Here are some examples of custom visualization.
From Our Blog
Evolution of big data overlaps with the evolution of cloud to a large extent. What both these movements have changed is who gets to eat and who gets to starve. Who means three players here: hardware vendors, software vendors and consulting companies. Let's start with commoditization of hardware. 30 years back Microsoft took a bet that hardware will get commoditized ... More
EMC may not be successful in it's big data strategy but one thing the are successful for sure is coining the term 'Data Lake'. As big data movement is evolving, it's looking more and more like a lake. Gartner in it's most recent hype curve, threw big data out and it created some FUD in market. There were discussion about ... More
Project Tungsten starting with Spark version 1.4 is the initiative to bring Spark closer to bare metal. The goal of project Tungsten is to substantially improve the memory and CPU efficiency of Spark applications and pushing the limits of underlying hardware. In distributed systems, conventional wisdom has been to always optimize network I/O, as that has been the most scarce and ... More
Hadoop has started with data locality as one it's primary features. Compute happens on a node where data is stored, it reduces data which needs to be shuffled over the network. Since every commodity machine has some basic compute power, you do not need specialized hardware and it brings the cost to a fraction of what it would be otherwise. ... More
For Spark 1.3 onwards, JdbcRDD is not recommended as DataFrames have support to load JDBC. Let us look at a simple example in this recipe. Using JdbcRDD with Spark is slightly confusing, so I thought about putting a simple use case to explain the functionality. Most probably you'll use it with spark-submit but I have put it here in spark-shell to illustrate ... More
For Spark 1.3 onward, JdbcRDD is not recommended as DataFrames have support to load JDBC. Let us look at a simple example in this recipe. Using JdbcRDD with Spark is slightly confusing, so I thought about putting a simple use case to explain the functionality. Most probably you'll use it with spark-submit but I have put it here in spark-shell to illustrate ... More