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IBM BigInsights is an analytics and data visualization tool leveraging hadoop.

IBM Analytics Engine

Hadoop-Related

3  IBM Analytics Engine - Enabling logging using - "platform logs" Log Analysis Instance

2  IBM Analytics Engine View Instance Details

Likelihood to Recommend

IBM is a global technology and innovation company headquartered in Armonk, NY.  It is one of the largest technology and consulting employers in the world, with approximately 427,000 employees serving clients in 170 countries. IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and advanced servers and supercomputers. Utilizing its business consulting, technology and R&amp;D expertise, IBM says it helps clients become "smarter" as the planet becomes more digitally interconnected. This includes working with organizations and governments to build systems that improve traffic congestion, availability of clean water, and the health and safety of populations. IBM invests more than $6 billion a year in R&amp;D, just completing its 18th year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame. The company was behind the inventions of the PC; SABRE travel reservation system; UPC codes, Watson, the Jeopardy!-playing computing system, and much more.

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IBM Analytics Engine 2021-01-14 07:20:23

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IBM Analytics Engine 2019-03-07 12:00:25

IBM Analytics Engine 2019-02-07 07:53:34

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Hadoop-Related Software

We have recently started utilizing IBM Analytics &amp; so far it has been a good experience. Our Data Intelligence team along with the DevOps team makes use of this tool as they deal with an enormous amount of data on a daily basis. The majority use it analyze the overall performance of all the applications related to Hadoop by gauging the datasets &amp; check for any inconsistency in their behavior.

Easy-to-use User interface with featured pack functionalities.
Scalable & customizable according to user needs.
Powerful yet simple cluster administration.
Affordable Pricing.

Could improve upon step-by-step instructions & make it more comprehensive.
Can be more prompt with their customer service.
Could come up with an e learning module for using the tool.

Saved considerable amount of time dealing with clustering & its administration.
Seamless & faster processing time helped us increasing ROI in return.
Decreases the need of relying on multiple tools for data analytics.

We used it for a research project to implement big data analysis on a YELP dataset from 2017 to answer a sales related question. We used the default Ambari file browser to upload the data, Hadoop ecosystem for data cleaning, Hive to query the dataset followed by TABLEAU for visualization.

Simple to implement
Good support from IBM
Easy to use
Widely used for Big data implementation

Even for L4 level support, assistance could have been more prompt. Since we are doing this for master level research all the students faced an issue when there was a MIGRATION to a newer version of the analytical engine, which doesn't let us take cmd line.
The explanation was not clear enough when a problem is resolved followed by a service request. Apart from being confidential, it's better for the students to understand why an issue occurs and what steps were take to resolve it.

This is a research project so nonprofit, but its easy to use and teach others

We use [IBM Analytics] to provision, manage, run and retire Apache Hadoop and Apache Spark clusters. With its calculation engine, this enterprise performance 
management solution helps the users move beyond the limits of 
spreadsheets, automating the planning process to drive faster, more 
accurate results. We use it to unify data sources into one single 
repository, because it enables users to build sophisticated, multidimensional 
models that drive forecasts. So far we haven't had any problems with the software.

Jobs with Spark, Hadoop, or Hive queries are rapidly attained
Can collect, organize and analyze your data accurately
You can customize, for example, Spark or Hadoop configuration settings, or Python, R, Scala, or Java libraries.

I find most of the functions user friendly and most importantly best fits in multi business setup and users easily adapt to the system. Also, you get a lot done in a very short period of time.

Simple to Utilize
Analyzes data with accuracy
Collects data rapidly
Manages data very well with no errors

In the time of huge information investigation, it's insufficient to simply endure – organizations need to rival and separate themselves from the rest. The individuals who can saddle information will be the best and improve their return for money invested from huge information examination. It's critical to look past the stages and rather center around the right-time admittance to information. At the point when utilized effectively, groups from lines of business to the C-suite will flourish by utilizing experiences inferred to better their center activities.

Google BigQuery, Amazon EMR (Elastic MapReduce), Snowflake

We have just begun to explore very large data sets and gave the IBM Analytics Engine a test to see how it would work for us. We were quite surprised at how complete and easy it was to set up and manage multiple Spark and Hadoop clusters. The separation of computing and storage really drives the performance up and gives you a better architecture with which to manage your overall big data plans.

It’s easy to integrate if you are already on IBM cloud, and even if you are not you can still explore the whole package with their free Lite plan and some service credits they offer.
Unlimited clusters without any performance degradation is a nice selling point. The smaller cluster size seems to work since it is only the computer, and not mixed with storage.
The connection to Watson Studio helps manage your jobs as you submit them to the cluster, and this is a nice easy relationship with Analytics Engine.

Easier pricing and plug-and-play like you see with AWS and Azure, it would be nice from a budgeting and billing standpoint, as well as better support for the administration.
Bundling of the Cloud Object Storage should be included with the Analytics Engine.
The inability to add your own Hadoop stack components has made some transfers a little more complex.

It has saved us quite a bit of time managing our catalog of clusters and keeping things organized.
Since we had a division we acquired running IBM Cloud, it was easy to get it running and try it out, but we found we prefer our Azure configuration better simply to keep our technology in alignment across corporate functions.
I definitely see some cost savings by separating out the storage and compute. It helps you start to put an appropriate price tag on certain instances of big data.

Azure HDInsight, AWS Cloud9, Snowflake and Oracle Advanced Analytics

Oracle Advanced Analytics, Snowflake, Salesforce Analytics Cloud, Google Maps API, MapAnything, MapAnything Live, Google Ads (formerly AdWords), Google Ad Manager, Google Analytics, Google Analytics Premium, Google BigQuery, Google Forms, Google Tag Manager, Azure DNS, Microsoft 365 Business, Microsoft Access, Microsoft Azure Machine Learning Workbench, Microsoft Network Monitor, Microsoft R (formerly Revolution R), Microsoft Silverlight, Dropbox, Dropbox Business, Google Drive, Alteryx Connect, Alteryx Analytics, Alteryx Analytics Gallery, SQL Server Business Intelligence Manager, SQL Server Integration Services, Microsoft SQL Server

IBM Analytics Engine

Overview

What is IBM Analytics Engine?