Enlighten your Big Data with Apache Spark™?
Hadoop => Most cost effective and scalable system to store Big Data
All Data in One Big Data Lake
With the cost-effective storage Hadoop and other Big Data technologies provide, you can now store petabytes of data at a fraction of cost versus traditional EDW systems. It means you never have to purge any data and all data will be available online to conduct your analytics.
New Insights in Real-Time
This additional data you can now store will lead to completely new insights to provide your business the competitive edge it needs. Apache Spark, which is the one and only compute platform you require on Big Data storage, will help you obtain these insights in a fraction of the time it took with traditional systems.
This makes the goal of being a data-driven organization a reality. No more intuition-driven decisions are now needed, which might have led to less than perfect decisions in the past. Every decision your organization now makes will be well-vetted and well-tested against existing data.
Take a deeper look at compute magic.
Spark – The Unified Platform for Big Data Apps
Spark provides a single platform which has libraries for all of your Big Data compute needs.
No disparate compute tools, just libraries!
Over the years, multiple technologies have emerged to cater to different Big Data compute needs like Storm (Streaming), MapReduce, Hive (SQL-like interface), Pig (high-level scripting), Mahout (Machine Learning), etc.
These technologies came with their own set of features, as well as Challenges. Spark completely changed the game. It caters to different compute needs by simply providing right libraries. The following are the libraries which come with Spark bundled as standard:
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.
Ask about our free Big Data POC at no cost or obligation.
From Our Blog
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
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
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