Tag Archives: ibm.com

Top 5 Big Data Use Cases

1. Big Data Exploration

I don’t agree with the author’s category. He admits that this is a “one size fits all category”. Almost seems like he had four use cases, and decided to make it into five by says adding that you can search, visualize, and understand data from multiple sources to help decision making. Haven’t we been doing this all along, with whatever database tools we’ve had?

2. Enhanced 360 degree view of the customer

From my own experience I had a project where we did this for a call center. However, the key was that we did real time queries to generate the 360 degree view when the call center agent took the call from the customer. The problem there was that in order to produce the view in only a couple of seconds we were very limited in what sort of data we had access to, and how we could analyze this. The Big Data perspective of 360 degrees assumes that the Hadoop repository retains a persistent copy of the data, something that many organizations don’t want. For example, the data will likely not be real time. However, having a copy of the data, and having the time to crunch it in batch mode will give a deeper insight into the customer. Perhaps what’s needed is a hybrid of realtime and batch, sort of like what Twitter is doing with Storm.

3. Security/Intelligence Extension

Searching for past occurrences of fraud, or creating a predictive model of possible future occurrences is very much a batch operation, and Hadoop works well on this since the scope of the analysis is limited only by the depth of the data and the duration of operations upon it.

4. Operations Analysis

I think that the author’s example of the “internet of things” might be a stretch, but commingling and analysis of unstructured and/or semi-structured server  and application logs is a perfect use case for Hadoop. This is especially true if the log data streams in, so that the results of your analysis are updated as each batch cycle completes.

5. Data Warehouse Augmentation

Some data can be pre-processed in Hadoop before loading into a traditional data warehouse. Other data can be analyzed without needing to load into a data warehouse at all, where it might just clutter up other queries. Hadoop lets you dump everything in, and sort it out later. Data warehouses are intended to be kept tidy.

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Proposed updates to Hive to support ACID transactions

HortonWorks developed solutions to add into Hive the ability to update multiple records as a single transaction following the ACID model. Part of the complexity of transactional updates is that the data must be written to all applicable nodes before the transaction can be considered complete. The naming convention within HDFS folders includes a transaction ID so that both committed and uncommitted files persist until all portions of the transaction have been completed. Because the transaction ID is included, any read operations that occur before the transaction has completed will access the old data.

Why go through all of this work to add an ACID model to Hive rather than just use HBase, which already supports transactions. The primary reason is that HBase only supports Consistency at the level of a single row update, rather than with a larger set of operations. Without Consistency, there is no ACID. HortonWorks lists a few other reasons, but I’m discounting them because they are general reasons why they prefer Hive over HBase.

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