NoSQL Benchmarks NoSQL use cases NoSQL Videos NoSQL Hybrid Solutions NoSQL Presentations Big Data Hadoop MapReduce Pig Hive Flume Oozie Sqoop HDFS ZooKeeper Cascading Cascalog BigTable Cassandra HBase Hypertable Couchbase CouchDB MongoDB OrientDB RavenDB Jackrabbit Terrastore Amazon DynamoDB Redis Riak Project Voldemort Tokyo Cabinet Kyoto Cabinet memcached Amazon SimpleDB Datomic MemcacheDB M/DB GT.M Amazon Dynamo Dynomite Mnesia Yahoo! PNUTS/Sherpa Neo4j InfoGrid Sones GraphDB InfiniteGraph AllegroGraph MarkLogic Clustrix CouchDB Case Studies MongoDB Case Studies NoSQL at Adobe NoSQL at Facebook NoSQL at Twitter



HANA: All content tagged as HANA in NoSQL databases and polyglot persistence

SAP HANA revenue for Q4

Still in the field of in-memory databases, here’s a quote from Peter Goldmacher1 about SAP’s last quarter financial results:

[…] we note that about half of the revenue shortfall was in SAP’s much-hyped HANA business. While an interesting technology, we continue to believe that emerging flexible, low-cost data management solutions are dampening interest in premium solutions such as HANA.

The first part is the fact. The second part is the analyst’s opinion. While a quarter is not a trend, there still needs to be an explanation. And the one offered by the analyst sounds good to me.

  1. Peter Goldmacher: analyst at Cowen.  

Original title and link: SAP HANA revenue for Q4 (NoSQL database©myNoSQL)


The Market for Products Like HANA and Exadata

Larry Dignan summarizing a research by a Cowen analyst:

We believe the trend in compute is massively distributed commodity boxes, and while there is a market for products like HANA and Exadata, it is significantly smaller than optimistic descriptions by SAP and Oracle. We believe HANA revenue is being inflated by subjective product revenue allocation to HANA at the expense of its traditional Apps and BI businesses, and swapping HANA for unused licenses. We think this inflates seat count with no incremental cash to the firm. At some point investors will likely start to worry about the implications on the other 90% of license sales we expect will eventually turn negative. We believe the offense in this case is getting into the data management business with old product (Sybase) and high priced hardware (HANA). While the impact on the model is low, the reputational cost to management could be high.

I have to confess that I’ve never really understood what these estimations are built upon. Maybe if I got this, I’d start calling myself an analyst.

Original title and link: The Market for Products Like HANA and Exadata (NoSQL database©myNoSQL)


SAP HANA With an Embedded Application Server. And Web Server. And Development Environment

Good or bad idea?

The core concept of SAP HANA Extended Application Services is to embed a full featured application server, web server, and development environment within the SAP HANA appliance itself. However this isn’t just another piece of software installed on the same hardware as SAP HANA; instead SAP has decided to truly integrate this new application services functionality directly into the deepest parts of the SAP HANA database itself, giving it an opportunity for performance and access to SAP HANA differentiating features that no other application server has.


Original title and link: SAP HANA With an Embedded Application Server. And Web Server. And Development Environment (NoSQL database©myNoSQL)


Overview of Dremel-Like Solutions: Moving Beyond Hadoop for Big Data Needs

Until I learn more about the recently announced Cloudera Impala and Druid from Metamarkets, this article by Jaikumar Vijayan should offer—with some inherent mistakes1—a good overview of the solutions aiming to offer alternatives to the batch-processing nature of Hadoop:

  • Google Dremel (BigQuery)
  • Cloudera Impala
  • Metamarkets Druid
  • Nodeable StreamReduce
  • SAP HANA integrated with Hadoop, etc.

  1. Just an example: “If you can stand latencies of a few seconds, Hadoop is fine. But Hadoop MapReduce is never going to be useful for sub-second latencies”. Then “The technology [nb Google Dremel] can run queries over trillion-row data tables in seconds…”

    Maybe just one more: consider the title “Moving beyond Hadoop” and then the quote from Google’s Ju-kay Kwek: “Google uses Dremel in conjuction with MapReduce. […] Hadoop and Dremel are distributed computing technologies, but each was built to address very different problems.” 

Original title and link: Overview of Dremel-Like Solutions: Moving Beyond Hadoop for Big Data Needs (NoSQL database©myNoSQL)


About SAP HANA in Relative Terms

Curt Monash reports from his briefings with SAP:

SAP HANA has what sounds like a natural disk-based persistence strategy — logs, snapshots, and so on. SAP says that this is synchronous enough to give ACID compliance. For some hardware partners, those “disks” are actually Fusion I/O cards.

I rarely encounter such relative terms used to describe a database. Can I have a database that stores a good amount of my data at a superb speed using a bizarre cluster persistence strategy?

As a side note, according to what I’m reading around, SAP is betting a lot on HANA and sees in it the solution that will make SAP the second largest database vendor.

Original title and link: About SAP HANA in Relative Terms (NoSQL database©myNoSQL)


Why In-Memory Analytics Is Like Digital Photography

A great article about a type of products in search for market share.

Original title and link: Why In-Memory Analytics Is Like Digital Photography (NoSQL database©myNoSQL)


SAP HANA: In-Memory Analytical Appliance

Dennis Moore:

SAP HANA does manage data in memory, for nearly incredible performance in some applications, but it also manages to persist that data on disk, making it suitable for analytical applications and transactional applications – simultaneously.

SAP HANA architecture

The architecture diagram above doesn’t show anything uncommon: a good ecosystem and a (pretty classical?) storage engine with an in-memory layer—the Calc Engine and MDX support are not present though in a relational database engine.

But here is the problem:

In the short-term, it seems that SAP still struggles to generate references for HANA, other than in a narrow set of custom data-warehouse-type analytics.


When HANA is generally available […]

The way I read it is: even with selected clients HANA doesn’t seem to provide the promised value. The real question is why? Isn’t it cost effective? Doesn’t HANA bring enough innovation to solve real problems? Is the in-memory layer not enough for addressing the range of problems HANA is promising to solve? Is the competition providing better or more effective solutions?

Original title and link: SAP HANA: In-Memory Analytical Appliance (NoSQL database©myNoSQL)