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Enlighten your Big Data with Apache Spark™

Hadoop => Most reliable,scalable and cost effective Big Data storage
Spark => Lightening fast cluster computing
Hadoop + Spark => Real-time cost-effective Big Data System


  • Advisory

  • Implementation

  • Analytics

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 Consulting Technology company

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.

On-premise Implementation

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

Project Tungsten: Apache Spark

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

Is data locality really a virtue?

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

Spark: JDBC Using DataFrames

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

Spark: DataFrames and JDBC

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

We Oxygenate the Ecosystem

As InfoObjects is approaching 10 years of its founding, one question came to mind during my thinking time this morning. The question started with why InfoObjects? And very soon it changed into why the consulting business? This blog should be a good read for not only our customers but also for new joiners who make a decision to choose a ... More

Apache Spark Shining at Strata

This year Strata moved to San Jose from Santa Clara. A lot of things were different like a bigger expo hall, less parking, etc. What caught my attention was something different. This was the first time Apache Spark was put at the same level as Apache Hadoop. Till last year Apache Spark was considered one part of the Hadoop eco-system, like ... More