Top 7 Humblebrags in Hadoop ecosystem

As the Hadoop ecosystem has evolved, so have the vendors providing various Apache Hadoop based solutions. Through the various conversations, presentations and flyers, you will tend to notice a pattern of humblebrags that come along in these. It is important to read between the lines, assess the real vendor capability and then take a contracting

decision.

Definition: (noun) “a humblebrag is basically a specific type of bragging that masks the brag in a faux-humble guise.”

1. Our Hadoop stack is more interoperable supporting more platforms than any one else.

2. We enable your Hadoop and Big Data cluster in production faster than anyone else.

3. We support all your data analytics needs right from customer sales at store to CEO presentation to board.

4. There are thousands of people trained on this technology with a huge skilled talent pool on the nascent technology.

5. We analyzed x billion records per day for ABC customer helping them save ‘n’ million $ per day

6. We provide Integrated Data capabilities with third party analytic tools through our huge array of connectors listed in Partners section of web site.

7. Our unique proposition for real time - integrated - complex event - resilient - scalable - unstructured data - stream - processing relying on enterprise version - of - open source stack - in non commodity hardware - gives the most bang for the buck

As you read over the Top 7 humblebrag list above, if you felt a sense of déjà vu, well… all we say is act wisely and think smartly. You may reach out to your trusted partners while consulting on various options.

image courtesy: urbandictionary.com/zazzle.com

Comments

5 online tools in data visualization playground

While building up an analytics dashboard, one of the major decision points is regarding the type of charts and graphs that would provide better insight into the data. To avoid a lot of re-work later, it makes sense to try the various chart options during the requirement and design phase. It is probably a well known myth that existing tool options in any product can serve all the user requirements with just minor configuration changes. We all know and realize that code needs to be written to serve each customer’s individual needs. To that effect, here are 5 tools that could empower your technical and business teams to decide on visualization options during the requirement phase. Listed below are online tools for you to add data and use as playground. 1) Many Eyes : Many Eyes is a data visualization experiment by IBM Research and the IBM Cognos software group. This tool provides option to upload data sets and create visualizations including Scatter Plot, Tree Ma

Data deduplication tactics with HDFS and MapReduce

As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction. Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability. From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets. Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus o

In-memory data model with Apache Gora

Open source in-memory data model and persistence for big data framework Apache Gora™ version 0.3, was released in May 2013. The 0.3 release offers significant improvements and changes to a number of modules including a number of bug fixes. However, what may be of significant interest to the DynamoDB community will be the addition of a gora-dynamodb datastore for mapping and persisting objects to Amazon's DynamoDB . Additionally the release includes various improvements to the gora-core and gora-cassandra modules as well as a new Web Services API implementation which enables users to extend Gora to any cloud storage platform of their choice. This 2-part post provides commentary on all of the above and a whole lot more, expanding to cover where Gora fits in within the NoSQL and Big Data space, the development challenges and features which have been baked into Gora 0.3 and finally what we have on the road map for the 0.4 development drive. Introducing Apache Gora Although

HadoopSphere

Search HadoopSphere