FluidQuery

Eliminates the long delays involved in moving data from one analytics platform to another

Lets you more easily query data in new and unique combinations. So you gain insights your competition may not have.

I think the integration aspect in general is key. It allows more openness between systems. You have access to more systems. Today Hadoop and other structured stores and PDAs, tomorrow we see noSQL, etc.

Alex is right for bulk data movement. I think it is a good way to work around major ETL activity to positiion sets for specific use cases. I also like the ability to locally materialize data via CTAS and Select into

Users can get deeper analytics from more data across your Logical Data Warehouse. Check out my recent blog on LDW and IFQ: http://www.ibmbigdat...

Managing and exploit growing data volumes--bring any of the leading hadoop distros seamlessly into your LDW

Also, queryable archive. Many customers want the ability to access colder data without having to restore or take up expensive storage and I/O. Fluid Query lets you establish and query this whether it is plain text or compressed format.

Use fit-for-purpose platforms: PDA for the DW, your choice of Hadoop platforms for data refinery, data staging, data archive, etc. in the LDW

Modernize your data warehouse

access semi structured data

A big challenge I see is where can a customer go to get this type of value without having to pay for big $$ license fees and added infrastructure?

This is a newly added software component with PDA or Netezza systems. This is FREE. That's right FREE. Pick it up, install, configure and start using it today at ZERO cost. Can our competitors make this claim?

Fluid Query provides open access to otherwise disparate system in the Logical Data Warehouse. The ability to query Hadoop, dashDB, DB2, Oracle and other Netezza/ PureData for Analytic systems.

Fluid Query enables using more data sources from PureData System for Analytics queries

IBM Fluid Query 1.5 is a new feature upgrade to PureData System for Analytics. It enables unified query of both PDA and Hadoop.

Lets you get deeper insights using more data - to yield a competitive edge!

IFQ extends IBM PureData System for Analytics support for fast, structured queries, enabling querying of Hadoop and other database sources from the data warehouse.

If needed, you can get great data movement speeds to-from Hadoop and PureData

IFQ lets you perform tasks on the platform--PDA or Hadoop--best suited for the workload

That's right, James. Using basic View definitions or on-the-fly SQL from the command line you can ask questions and get answers from other systems.

IFQ supports storage of infrequently accessed data in Hadoop to extend the data warehouse.

Design is to move the query to the data, not the data to the query

Less effort -- designed to push the work to where the data resides -- is the thought process

With so much data Volume, moving less data around the Logical Data Warehouse is quite desirable

Fluid Query 1.0 connected PureData to BigInsights, Hortonworks and Cloudera

Fluid Query 1.0 also enabled fast data movement to these Hadoop sources and PureData

From a support perspective it is very easy to setup and leverage the functionality

Fluid Query expands the reach of PureData System for Analytics (PDA) to many other data sources

Think of this -- PDA SQL accessing semi-structured Hadoop data!

We have usage across a range industries. Healthcare, Gaming, Banking and Retail for example. Not to mention Insurance. I mean we have nearly all segments in action.

One not-too-far-fetched concept is to push Internet of Things machine data into PDA for further analysis

One of my clients has begun using #FluidQuery to store intermediate data from in-database geospatial analytics into #BigInsights

Some of our customers are focusing on capacity relief and on-boarding new sets of high value data onto PDA. By using bulk data movment, colder data can be pushed to Hadoop freeing up I/O, storage capacity and workloads.

Another has begun pulling data from an Oracle database into their production and delivery optimization processes

Many customers are establishing queryable archives. Now you don't have to chase historical or cold data through restores or replication. Moving data to Hadoop for example offer abiliy to access, combine or analyze on Hadoop.

@AlexHay01 - How cool is that? Querying #Oracle data from #Puredata!

I have another client exploring the combination of #PDA and #dashDB for new applications and using #FluidQuery to provide access to both data sets for reporting

Others are reaching across to other systems that store data and may have dimensioned data. They supplement local data on your Netezza system via View definitions. Doing this with Oracle, Hadoop and Spark.

We had one customer export data from Hadoop to PDA and completed a 75 million row export in less than 5 minutes. That is due to moving data in Netezza binary format. Definite uplift over out of the box Apach Sqoop b/w PDA and Hadoop.

And on top of all these great uses cases, it's easy to get going on #FluidQuery.

So customers also love to query compressed data stroed on Hadoop.

Some customers are taking a closer look at their own Netezza footprint and evaluating ways to establish an analytics hub all to its own. So, in absence of Hadoop, resetting usage for their PDA environment.

Some customers transform their web logs on Hadoop into Hive formatted SQL data and then either query or combine with local PDA data to drive new insights or programs discovered by applying predictive analytics

it fits quite nicely by integrating various data stores in the Logical Data Warehouse

All of the fit for purpose stores of a #LDW are queryable using #PureData with #FluidQuery

LDW is all about openness and access. Leveraging value across the enterprise. Key to this is taking advantage of local processing. When I ask a question in LDW, I want it to be seamless to my app and processed remotely.

Fluid query is lets you access, optimize queries across, and move data bilaterally between the key platforms in your LDW--the DW and Hadoop--to meet needs for efficient archival, capacity management, and disaster recovery

As @jameskobielus said, "IFQ lets you perform tasks on the platform--PDA or Hadoop--best suited for the workload" which is what the LDW is really all about

Fluid Query allows easy queries from PDA to Oracle and DB2 -- and PDA to other PDA appliances

Relative to IBM's overall capability, we have a portfolio of products that are enabled for this today. IBM's BigInsights Big SQL, DB2, PureData for Operational Analytics and now IBM Fluid Query for use with Netezza and PureData for Analtyics

Fluid query binds the zones in your multi-zone LDW into a unified resource for delivering downstream insights based on all data--structured and unstructured--and executing all analytics, ranging from BI to predictive to etc.

@ddailey2012 Exactly Doug, #FluidQuery is an underpinning of the LDW in that it abstracts the data access from the "plumbing"

So we can connect the dots internal to IBM and we can tether out to other stores.