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To install CouchDB, we need to have required collections of packages installed on our systems that CouchDB depends on. First do a full system update and then install needed dependencies using yum or apt package manager. For more information on how to create a database and manage their settings visit THIS PAGE, or stay tuned for our next series of articles on CouchDB. If you have any questions or problems regarding this article and want help within 24 Hours?
Well documented and worked without any glitches, except i need to install the latest SpiderMonkey instead of the one listed here. Just as a heads up though; having run the CouchDB instance from EPEL (and a few other sources) i was quite frustrated with running a seriously old version – Erlang included! I published an article on how to package a standalone RPM for CentOS 6 which you might find useful? All software is stored on a secure version control system, which provides safe location to host all required software data. Transferring data from one database to the other can be done fast, quick and automatic without making mistakes by entering data manually on another system. We do code review for our own software applications made in house, but also for your custom made software (applications, websites or other).
We have an active codebase with different configurable components, which can be integrated into any software application. Not yet a need for advanced software applications, maybe a simple, maintainable and scalable site is what you need right now? This entry was posted in Big Data, CouchDB, DynamoBI, General BI, Open Source on August 29, 2011 by ngoodman. BI Tools are commoditized; consider all the great alternatives available inexpensively (either in Open Source for free, Open Core, or even simply proprietary).
Driving the developer out (as much as possible) is one of the best reasons to try and enable your cool, CouchDB views via SQL. We can easily use a familiar query language, SQL, that allows for aggregation, filtering, and limiting.
This entry was posted in Big Data, CouchDB, DynamoBI, Uncategorized on June 22, 2011 by ngoodman. Following up on my first post on an alternative, more SQL-eee metadata driven approach to doing BI on Big Data, I’d like to share an example on how we can enable easy reporting on top of BIg Data immediately for CouchDB users. We’ve been working with some new potential customers on how to leverage their investment in Big Data (specifically Big Couch provided by the fine folks at Cloudant. I figured I could generalize what we’re doing at this prospect to a more general post, and hope that this post (and the software behind it, namely LucidDB) finds there way into the hands of others that are trying to do Ad Hoc and easy SQL based analytics on top of CouchDB. We’ve created a solution that allows HTTP views in CouchDB to be made accessible via SQL inside of LucidDB.
There’s another key piece of our philosophy on Big Data BI that is also present here. I won’t spend that much more time delving into the specifics of all the reasons that people are going so ape-sh*t over Big Data. It’s just a bunch of technology that propeller heads (I am one myself) sling code with that crunch data to get data into custom built reporting type applications.


Until businesses get to ACTUALLY USE Big Data systems (and not via proxy built IT applications) it’s value to the business will be minimal. The total number of events can be represented and aggregated to many many different levels of aggregation.
For dashboards the user is almost certainly willing to use the previous load (last hours) data instead of waiting 17minutes to run in the Big Data system.
We’re building a system, with LucidDB at the center, that is the most complete solution for doing real, day to day, full featured (adhoc, metadata, etc), inexpensive analytics on top of Big Data.
Ability to use inexpensive, commodity BI tools on top of Big Data directly and cached (Pentaho, BIRT, etc). Easy, RESTful interaction with the server so our solution fits nicely with your existing systems. As I mentioned above in requirement section, that we need to have dependencies installed on our server before compiling CouchDB. Once all the dependencies are satisfied, you can download and install CouchDB from source using following commands. Once CouchDB compiled successfully, it’s time to create a new CouchDB user, grant correct permissions and add the startup scripts. Verify the CouchDB by going to the below URL, there will see a Welcome page that displays the following message. I used your install instructions on an inmotion hosting VPS running CentOS 6.11 after finding no joy trying those at the CouchDB website. From development of specialized software to delivering useful IT solutions for small businesses (SME's) and starters. Together we can define all the software requirements and have predefined feedback along the project development.
Allowing easy SQL access (including super fast, analytic database cached views) is a big win for reducing load *AND* increasing usability of data in NoSQL systems.
Regardless of what solution you choose, these tools have fantastic, easy to use capabilities that are very easy for business users to build their own reports. Once we have that CouchDB view in LucidDB then Pentaho (or other BI tools) can connect and access, do ad hod reporting like they always have).
For instance, the ability to filter to a particular key (where key = XYZ) pushed down, or group_level=*. In particular, this prospects users are thrilled with their dashboards and analytics that are baked into their application, but they need to be able to enable Ad Hoc, easy analysis on top of their data via a traditional reporting tool. Cached and summarized inside LucidDB -OR- up to date, real time results from Big Data (aka CouchDB). We’ve used the Federal Purchasing Dataset (a sample of 1000 records, since this runs on my laptop). Unlike SQL databases, they’re NOT ACCESSIBLE to analysts, and reporting tools for easy report authoring and for businesses to quickly and easily write reports. When businesses get to use Big Data systems directly; there will be dramatic benefit to the business in terms of timeliness, decision making, and insights. When I see this thing called “Average Bid Amount” on my report, which fields back in my NoSQL datastore was that calculated from? Some, very highly summarized figures (such as daily event counts) should be kept in memory and accessible extremely fast for dashboards.


In short, BI is a huge win for companies because it’s cheaper, helps get insights faster, and ultimately allows analysts to be somewhat self sufficient to do their job. Having a system that is doing BI on top of Big Data needs a way to easily, in a metadata driven fashion, match up the Big Data with other reporting only data. For Ad Hoc analysis reports need to be speed of thought; big data systems can do OK here, but on smaller datasets the best choice is a columnar, high performance BI database (ahem!). CouceDB uses JSON to store data with documents, which you can access from a web browser via HTTP.
For someone looking to install CouchDB on a multitude of RHEL 5 systems, the title is misleading. As a certified database for Pentaho, you can be quite comfortable that Pentaho will work very very well with LucidDB. Like any organization, IT is overworked and reports always seem to find their way to close to the back of the priority. Here’s a couple of screenshots from Futon that show the source data docs, and then the CouchDB View. Desperate to figure out a more elegant solution to self service BI on top of CouchDB, Hive, etc? Why bother with a separate OLAP modeling tool when you already know your aggregation methods and data types. Big Data has taken the industry back 10-15 years by making not just complicated reports but literally EVERY report be built by a developer!
Current solutions require complex ETL development and assemble it as part of that stream of data to shove at a MySQL database. Since Hadoop and Big Data is hype-du-jour I don’t expect you to believe my bold statements. Our connector initially supports streaming data from the values, but we’re already working on getting proper support for the KEYs (and ability to push down group_level, etc). We have not done all this additional metadata work for the CouchDB connector yet; or actually, how to get ALL this information from the remote view as well.
Datamarts (star schemas) that represent some first or second level of aggregation (100m rows) should be kept in a fast column store database.
Current solutions allow for direct connect in reports to Big Data systems but you still have to write programs to access the data. Other solutions simply pull data out of Big Data systems and shove it at MySQL because of this exact reason! Current solutions provide only tools for data slinging between these levels of aggregation; none provide the ability to access and manage them in a single system.
How about hiring someone to build reports who knows the ins and outs of cluster management in Hadoop, knows how to write multi step programs and write application code to push that data into visualization components in the application?



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