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  • Data Warehouse Best Practices

    - by jean-pierre.dijcks
    In our quest to share our endless wisdom (ahem…) one of the things we figured might be handy is recording some of the best practices for data warehousing. And so we did. And, we did some more… We now have recreated our websites on Oracle Technology Network and have a separate page for best practices, parallelism and other cool topics related to data warehousing. But the main topic of this post is the set of recorded best practices. Here is what is available (and it is a series that ties together but can be read independently), applicable for almost any database version: Partitioning 3NF schema design for a data warehouse Star schema design Data Loading Parallel Execution Optimizer and Stats management The best practices page has a lot of other useful information so have a look here.

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  • Auto DOP and Concurrency

    - by jean-pierre.dijcks
    After spending some time in the cloud, I figured it is time to come down to earth and start discussing some of the new Auto DOP features some more. As Database Machines (the v2 machine runs Oracle Database 11.2) are effectively selling like hotcakes, it makes some sense to talk about the new parallel features in more detail. For basic understanding make sure you have read the initial post. The focus there is on Auto DOP and queuing, which is to some extend the focus here. But now I want to discuss the concurrency a little and explain some of the relevant parameters and their impact, specifically in a situation with concurrency on the system. The goal of Auto DOP The idea behind calculating the Automatic Degree of Parallelism is to find the highest possible DOP (ideal DOP) that still scales. In other words, if we were to increase the DOP even more  above a certain DOP we would see a tailing off of the performance curve and the resource cost / performance would become less optimal. Therefore the ideal DOP is the best resource/performance point for that statement. The goal of Queuing On a normal production system we should see statements running concurrently. On a Database Machine we typically see high concurrency rates, so we need to find a way to deal with both high DOP’s and high concurrency. Queuing is intended to make sure we Don’t throttle down a DOP because other statements are running on the system Stay within the physical limits of a system’s processing power Instead of making statements go at a lower DOP we queue them to make sure they will get all the resources they want to run efficiently without trashing the system. The theory – and hopefully – practice is that by giving a statement the optimal DOP the sum of all statements runs faster with queuing than without queuing. Increasing the Number of Potential Parallel Statements To determine how many statements we will consider running in parallel a single parameter should be looked at. That parameter is called PARALLEL_MIN_TIME_THRESHOLD. The default value is set to 10 seconds. So far there is nothing new here…, but do realize that anything serial (e.g. that stays under the threshold) goes straight into processing as is not considered in the rest of this post. Now, if you have a system where you have two groups of queries, serial short running and potentially parallel long running ones, you may want to worry only about the long running ones with this parallel statement threshold. As an example, lets assume the short running stuff runs on average between 1 and 15 seconds in serial (and the business is quite happy with that). The long running stuff is in the realm of 1 – 5 minutes. It might be a good choice to set the threshold to somewhere north of 30 seconds. That way the short running queries all run serial as they do today (if it ain’t broken, don’t fix it) and allows the long running ones to be evaluated for (higher degrees of) parallelism. This makes sense because the longer running ones are (at least in theory) more interesting to unleash a parallel processing model on and the benefits of running these in parallel are much more significant (again, that is mostly the case). Setting a Maximum DOP for a Statement Now that you know how to control how many of your statements are considered to run in parallel, lets talk about the specific degree of any given statement that will be evaluated. As the initial post describes this is controlled by PARALLEL_DEGREE_LIMIT. This parameter controls the degree on the entire cluster and by default it is CPU (meaning it equals Default DOP). For the sake of an example, let’s say our Default DOP is 32. Looking at our 5 minute queries from the previous paragraph, the limit to 32 means that none of the statements that are evaluated for Auto DOP ever runs at more than DOP of 32. Concurrently Running a High DOP A basic assumption about running high DOP statements at high concurrency is that you at some point in time (and this is true on any parallel processing platform!) will run into a resource limitation. And yes, you can then buy more hardware (e.g. expand the Database Machine in Oracle’s case), but that is not the point of this post… The goal is to find a balance between the highest possible DOP for each statement and the number of statements running concurrently, but with an emphasis on running each statement at that highest efficiency DOP. The PARALLEL_SERVER_TARGET parameter is the all important concurrency slider here. Setting this parameter to a higher number means more statements get to run at their maximum parallel degree before queuing kicks in.  PARALLEL_SERVER_TARGET is set per instance (so needs to be set to the same value on all 8 nodes in a full rack Database Machine). Just as a side note, this parameter is set in processes, not in DOP, which equates to 4* Default DOP (2 processes for a DOP, default value is 2 * Default DOP, hence a default of 4 * Default DOP). Let’s say we have PARALLEL_SERVER_TARGET set to 128. With our limit set to 32 (the default) we are able to run 4 statements concurrently at the highest DOP possible on this system before we start queuing. If these 4 statements are running, any next statement will be queued. To run a system at high concurrency the PARALLEL_SERVER_TARGET should be raised from its default to be much closer (start with 60% or so) to PARALLEL_MAX_SERVERS. By using both PARALLEL_SERVER_TARGET and PARALLEL_DEGREE_LIMIT you can control easily how many statements run concurrently at good DOPs without excessive queuing. Because each workload is a little different, it makes sense to plan ahead and look at these parameters and set these based on your requirements.

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  • My Take on Hadoop World 2011

    - by Jean-Pierre Dijcks
    I’m sure some of you have read pieces about Hadoop World and I did see some headlines which were somewhat, shall we say, interesting? I thought the keynote by Larry Feinsmith of JP Morgan Chase & Co was one of the highlights of the conference for me. The reason was very simple, he addressed some real use cases outside of internet and ad platforms. The following are my notes, since the keynote was recorded I presume you can go and look at Hadoopworld.com at some point… On the use cases that were mentioned: ETL – how can I do complex data transformation at scale Doing Basel III liquidity analysis Private banking – transaction filtering to feed [relational] data marts Common Data Platform – a place to keep data that is (or will be) valuable some day, to someone, somewhere 360 Degree view of customers – become pro-active and look at events across lines of business. For example make sure the mortgage folks know about direct deposits being stopped into an account and ensure the bank is pro-active to service the customer Treasury and Security – Global Payment Hub [I think this is really consolidation of data to cross reference activity across business and geographies] Data Mining Bypass data engineering [I interpret this as running a lot of a large data set rather than on samples] Fraud prevention – work on event triggers, say a number of failed log-ins to the website. When they occur grab web logs, firewall logs and rules and start to figure out who is trying to log in. Is this me, who forget his password, or is it someone in some other country trying to guess passwords Trade quality analysis – do a batch analysis or all trades done and run them through an analysis or comparison pipeline One of the key requests – if you can say it like that – was for vendors and entrepreneurs to make sure that new tools work with existing tools. JPMC has a large footprint of BI Tools and Big Data reporting and tools should work with those tools, rather than be separate. Security and Entitlement – how to protect data within a large cluster from unwanted snooping was another topic that came up. I thought his Elephant ears graph was interesting (couldn’t actually read the points on it, but the concept certainly made some sense) and it was interesting – when asked to show hands – how the audience did not (!) think that RDBMS and Hadoop technology would overlap completely within a few years. Another interesting session was the session from Disney discussing how Disney is building a DaaS (Data as a Service) platform and how Hadoop processing capabilities are mixed with Database technologies. I thought this one of the best sessions I have seen in a long time. It discussed real use case, where problems existed, how they were solved and how Disney planned some of it. The planning focused on three things/phases: Determine the Strategy – Design a platform and evangelize this within the organization Focus on the people – Hire key people, grow and train the staff (and do not overload what you have with new things on top of their day-to-day job), leverage a partner with experience Work on Execution of the strategy – Implement the platform Hadoop next to the other technologies and work toward the DaaS platform This kind of fitted with some of the Linked-In comments, best summarized in “Think Platform – Think Hadoop”. In other words [my interpretation], step back and engineer a platform (like DaaS in the Disney example), then layer the rest of the solutions on top of this platform. One general observation, I got the impression that we have knowledge gaps left and right. On the one hand are people looking for more information and details on the Hadoop tools and languages. On the other I got the impression that the capabilities of today’s relational databases are underestimated. Mostly in terms of data volumes and parallel processing capabilities or things like commodity hardware scale-out models. All in all I liked this conference, it was great to chat with a wide range of people on Oracle big data, on big data, on use cases and all sorts of other stuff. Just hope they get a set of bigger rooms next time… and yes, I hope I’m going to be back next year!

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  • Live from ODTUG - Big Data and SQL session #2

    - by Jean-Pierre Dijcks
    Sitting in Dominic Delmolino's session at ODTUG (KScope 12). If the session count at conferences is any indication then we will see more and more people start to deploy MapReduce in the database. And yes, that would be with SQL and PL/SQL first and foremost. Both Dominic and our own Bryn Llewellyn are doing MapReduce in the database presentations.  Since I have seen both, I would advice people to first look through Dominic's session to get a good grasp on what mappers do and what reducers do, then dive into Bryn's for a bunch of PL/SQL example. The thing I like about Dominic's is the last slide (a recursive WITH statement) to do this in SQL... Now I am hoping that next year we will see tools vendors show off how they work with Hadoop and MapReduce (at least talking about the concepts!!).

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  • Serial plans: Threshold / Parallel_degree_limit = 1

    - by jean-pierre.dijcks
    As a very short follow up on the previous post. So here is some more on getting a serial plan and why that happens Another reason - compared to the auto DOP is not on as we looked at in the earlier post - and often more prevalent to get a serial plan is if the plan simply does not take long enough to consider a parallel path. The resulting plan and note looks like this (note that this is a serial plan!): explain plan for select count(1) from sales; SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY()); PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------- Plan hash value: 672559287 -------------------------------------------------------------------------------------- | Id  | Operation            | Name  | Rows  | Cost (%CPU)| Time     | Pstart| Pstop | -------------------------------------------------------------------------------------- PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------- |   0 | SELECT STATEMENT     |       |     1 |     5   (0)| 00:00:01 |       |     | |   1 |  SORT AGGREGATE      |       |     1 |            |          |       |     | |   2 |   PARTITION RANGE ALL|       |   960 |     5   (0)| 00:00:01 |     1 |  16 | |   3 |    TABLE ACCESS FULL | SALES |   960 |     5   (0)| 00:00:01 |     1 |  16 | Note -----    - automatic DOP: Computed Degree of Parallelism is 1 because of parallel threshold 14 rows selected. The parallel threshold is referring to parallel_min_time_threshold and since I did not change the default (10s) the plan is not being considered for a parallel degree computation and is therefore staying with the serial execution. Now we go into the land of crazy: Assume I do want this DOP=1 to happen, I could set the parameter in the init.ora, but to highlight it in this case I changed it on the session: alter session set parallel_degree_limit = 1; The result I get is: ERROR: ORA-02097: parameter cannot be modified because specified value is invalid ORA-00096: invalid value 1 for parameter parallel_degree_limit, must be from among CPU IO AUTO INTEGER>=2 Which of course makes perfect sense...

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  • Explaining Explain Plan Notes for Auto DOP

    - by jean-pierre.dijcks
    I've recently gotten some questions around "why do I not see a parallel plan" while Auto DOP is on (I think)...? It is probably worthwhile to quickly go over some of the ways to find out what Auto DOP was thinking. In general, there is no need to go tracing sessions and look under the hood. The thing to start with is to do an explain plan on your statement and to look at the parameter settings on the system. Parameter Settings to Look At First and foremost, make sure that parallel_degree_policy = AUTO. If you have that parameter set to LIMITED you will not have queuing and we will only do the auto magic if your objects are set to default parallel (so no degree specified). Next you want to look at the value of parallel_degree_limit. It is typically set to CPU, which in default settings equates to the Default DOP of the system. If you are testing Auto DOP itself and the impact it has on performance you may want to leave it at this CPU setting. If you are running concurrent statements you may want to give this some more thoughts. See here for more information. In general, do stick with either CPU or with a specific number. For now avoid the IO setting as I've seen some mixed results with that... In 11.2.0.2 you should also check that IO Calibrate has been run. Best to simply do a: SQL> select * from V$IO_CALIBRATION_STATUS; STATUS        CALIBRATION_TIME ------------- ---------------------------------------------------------------- READY         04-JAN-11 10.04.13.104 AM You should see that your IO Calibrate is READY and therefore Auto DOP is ready. In any case, if you did not run the IO Calibrate step you will get the following note in the explain plan: Note -----    - automatic DOP: skipped because of IO calibrate statistics are missing One more note on calibrate_io, if you do not have asynchronous IO enabled you will see:  ERROR at line 1: ORA-56708: Could not find any datafiles with asynchronous i/o capability ORA-06512: at "SYS.DBMS_RMIN", line 463 ORA-06512: at "SYS.DBMS_RESOURCE_MANAGER", line 1296 ORA-06512: at line 7 While this is changed in some fixes to the calibrate procedure, you should really consider switching asynchronous IO on for your data warehouse. Explain Plan Explanation To see the notes that are shown and explained here (and the above little snippet ) you can use a simple explain plan mechanism. There should  be no need to add +parallel etc. explain plan for <statement> SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY()); Auto DOP The note structure displaying why Auto DOP did not work (with the exception noted above on IO Calibrate) is like this: Automatic degree of parallelism is disabled: <reason> These are the reason codes: Parameter -  parallel_degree_policy = manual which will not allow Auto DOP to kick in  Hint - One of the following hints are used NOPARALLEL, PARALLEL(1), PARALLEL(MANUAL) Outline - A SQL outline of an older version (before 11.2) is used SQL property restriction - The statement type does not allow for parallel processing Rule-based mode - Instead of the Cost Based Optimizer the system is using the RBO Recursive SQL statement - The statement type does not allow for parallel processing pq disabled/pdml disabled/pddl disabled - For some reason (alter session?) parallelism is disabled Limited mode but no parallel objects referenced - your parallel_degree_policy = LIMITED and no objects in the statement are decorated with the default PARALLEL degree. In most cases all objects have a specific degree in which case Auto DOP will honor that degree. Parallel Degree Limited When Auto DOP does it works you may see the cap you imposed with parallel_degree_limit showing up in the note section of the explain plan: Note -----    - automatic DOP: Computed Degree of Parallelism is 16 because of degree limit This is an obvious indication that your are being capped for this statement. There is one quite interesting one that happens when you are being capped at DOP = 1. First of you get a serial plan and the note changes slightly in that it does not indicate it is being capped (we hope to update the note at some point in time to be more specific). It right now looks like this: Note -----    - automatic DOP: Computed Degree of Parallelism is 1 Dynamic Sampling With 11.2.0.2 you will start seeing another interesting change in parallel plans, and since we are talking about the note section here, I figured we throw this in for good measure. If we deem the parallel (!) statement complex enough, we will enact dynamic sampling on your query. This happens as long as you did not change the default for dynamic sampling on the system. The note looks like this: Note ----- - dynamic sampling used for this statement (level=5)

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  • Big Data Videos

    - by Jean-Pierre Dijcks
    You can view them all on YouTube using the following links: Overview for the Boss: http://youtu.be/ikJyrmKdJWc Hadoop: http://youtu.be/acWtid-OOWM Acquiring Big Data: http://youtu.be/TfuhuA_uaho Organizing Big Data: http://youtu.be/IC6jVRO2Hq4 Analyzing Big Data: http://youtu.be/2yf_jrBhz5w These videos are a great place to start learning about big data, the value it can bring to your organisation and how Oracle can help you start working with big data today.

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  • Interesting sessions/tips from RMOUG

    - by jean-pierre.dijcks
    One of the sessions I was at at last week's RMOUG was a session on Temp Tablespace Groups. I had a look because I had no experience with this and it seemed to help with parallel processing and the allocation/usage of temp. You can read the excellent write-up at Kellyn Pedersen's blog - who did the session and all the work - here. So for all of those who may be seeing lot's of waits like enq: TS - Contention when you are doing hash joins and sorts, do have a look at the above blog post. I also had the chance to listen in at Stewart Bryson's session on Restartability (he had 3 R-s) where he gave very useful tips about how to deal with your data warehouse loads. Questions like archive log mode - should I or should I not were well covered. Flashback archives, also nice to hear about. Very nice talk, very interesting. Unfortunately he hasn't blogged about it yes, so no pointers to that one. Got to see a couple of other interesting sessions, and as conferences go got to meet some interesting Oracle folks from the region. As usual RMOUG was useful and fun. Off to the drawing boards to design next year's session!

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  • Big Data Appliance X4-2 Release Announcement

    - by Jean-Pierre Dijcks
    Today we are announcing the release of the 3rd generation Big Data Appliance. Read the Press Release here. Software Focus The focus for this 3rd generation of Big Data Appliance is: Comprehensive and Open - Big Data Appliance now includes all Cloudera Software, including Back-up and Disaster Recovery (BDR), Search, Impala, Navigator as well as the previously included components (like CDH, HBase and Cloudera Manager) and Oracle NoSQL Database (CE or EE). Lower TCO then DIY Hadoop Systems Simplified Operations while providing an open platform for the organization Comprehensive security including the new Audit Vault and Database Firewall software, Apache Sentry and Kerberos configured out-of-the-box Hardware Update A good place to start is to quickly review the hardware differences (no price changes!). On a per node basis the following is a comparison between old and new (X3-2) hardware: Big Data Appliance X3-2 Big Data Appliance X4-2 CPU 2 x 8-Core Intel® Xeon® E5-2660 (2.2 GHz) 2 x 8-Core Intel® Xeon® E5-2650 V2 (2.6 GHz) Memory 64GB 64GB Disk 12 x 3TB High Capacity SAS 12 x 4TB High Capacity SAS InfiniBand 40Gb/sec 40Gb/sec Ethernet 10Gb/sec 10Gb/sec For all the details on the environmentals and other useful information, review the data sheet for Big Data Appliance X4-2. The larger disks give BDA X4-2 33% more capacity over the previous generation while adding faster CPUs. Memory for BDA is expandable to 512 GB per node and can be done on a per-node basis, for example for NameNodes or for HBase region servers, or for NoSQL Database nodes. Software Details More details in terms of software and the current versions (note BDA follows a three monthly update cycle for Cloudera and other software): Big Data Appliance 2.2 Software Stack Big Data Appliance 2.3 Software Stack Linux Oracle Linux 5.8 with UEK 1 Oracle Linux 6.4 with UEK 2 JDK JDK 6 JDK 7 Cloudera CDH CDH 4.3 CDH 4.4 Cloudera Manager CM 4.6 CM 4.7 And like we said at the beginning it is important to understand that all other Cloudera components are now included in the price of Oracle Big Data Appliance. They are fully supported by Oracle and available for all BDA customers. For more information: Big Data Appliance Data Sheet Big Data Connectors Data Sheet Oracle NoSQL Database Data Sheet (CE | EE) Oracle Advanced Analytics Data Sheet

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  • Conventional Parallel Inserts do Exist in Oracle 11

    - by jean-pierre.dijcks
    Had an interesting chat with Greg about said topic and searching showed the following link to discuss this topic in some detail (no reason for me to repeat this). insert /*+ noappend parallel(t1) */ into t1 select /*+ parallel(t2) */ * from t2 generates a load table conventional and does give you a parallel insert without doing a direct path insert. As this is missing from the official documentation it is probably something few people actually know existed, so kudos to Randolf Geist.

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  • Winner of the 2012 Government Big Data Solutions Award

    - by Jean-Pierre Dijcks
    Hot off the press: The winner of the 2012 Government Big Data Solutions Aware is the National Cancer Institute!! Read all the details on CTOLabs.com. A short excerpt to wet your appetite: "... This solution, based on the Oracle Big Data Appliance with the Cloudera Distribution of Apache Hadoop (CDH), leverages capabilities available from the Big Data community today in pioneering ways that can serve a broad range of researchers. The promising approach of this solution is repeatable across many other Big Data challenges for bioinfomatics, making this approach worthy of its selection as the 2012 Government Big Data Solution Award." Read the entire post. Congrats to the entire team!!

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  • Big data: An evening in the life of an actual buyer

    - by Jean-Pierre Dijcks
    Here I am, and this is an actual story of one of my evenings, trying to spend money with a company and ultimately failing. I just gave up and bought a service from another vendor, not the incumbent. Here is that story and how I think big data could actually fix this (and potentially prevent some of this from happening). In the end this story should illustrate how big data can benefit me (get me what I want without causing grief) and the company I am trying to buy something from. Note: Lots of details left out, I have no intention of being the annoyed blogger moaning about a specific company. What did I want to get? We watch TV, we have internet and we do have a land line. The land line is from a different vendor then the TV and the internet. I have decided that this makes no sense and I was going to get a bundle (no need to infer who this is, I just picked the generic bundle word as this is what I want to get) of all three services as this seems to save me money. I also want to not talk to people, I just want to click on a website when I feel like it and get it all sorted. I do think that is reality. I want to just do my shopping at 9.30pm while watching silly reruns on TV. Problem 1 - Bad links So, I'm an existing customer of the company I want to buy my bundle from. I go to the website, I click on offers. Turns out they are offers for new customers. After grumbling about how good they are, I click on offers for existing customers. Bummer, it goes to offers for new customers, so I click again on the link for offers for existing customers. No cigar... it just does not work. Big data solutions: 1) Do not show an existing customer the offers for new customers unless they are the same => This is only partially doable without login, but if a customer logs in the application should always know that this is an existing customer. But in general, imagine I do this from my home going through the internet service of this vendor to their domain... an instant filter should move me into the "existing customer route". 2) Flag dead or incorrect links => I've clicked the link for "existing customer offers" at least 3 times in under 5 seconds... Identifying patterns like this is easy in Hadoop and can very quickly make a list of potentially incorrect links. No need for realtime fixing, just the fact that this link can be pro-actively fixed across my entire web domain is a good thing. Preventative maintenance! Problem 2 - Purchase cannot be completed Apart from the fact that the browsing pattern to actually get to what I want is poorly designed, my purchase never gets past a specific point. In other words, I put something into my shopping cart and when I want to move on the application either crashes (with me going to an error page) or hangs or goes into something like chat. So I try again, and again and again. I think I tried this entire path (while being logged in!!) at least 10 times over the course of 20 minutes. I also clicked on the feedback button and, frustrated as I was, tried to explain this did not work... Big Data Solutions: 1) This web site does shopping cart analysis. I got an email next day stating I have things in my shopping cart, just click here to complete my purchase. After the above experience, this just added insult to my pain... 2) What should have happened, is a Hadoop job going over all logged in customers that are on the buy flow. It should flag anyone who is trying (multiple attempts from the same user to do the same thing), analyze the shopping card, the clicks to identify what the customers wants, his feedback provided (note: always own your own website feedback, never just farm this out!!) and in a short turn around time (30 minutes to 2 hours or so) email me with a link to complete my purchase. Not with a link to my shopping cart 12 hours later, but a link to actually achieve what I wanted... Why should this company go through the big data effort? I do believe this is relatively easy to do using our Oracle Event Processing and Big Data Appliance solutions combined. It is almost so simple (to my mind) that it makes no sense that this is not in place? But, now I am ranting... Why is this interesting? It is because of $$$$. After trying really hard, I mean I did this all in the evening, and again in the morning before going to work. I kept on failing, But I really wanted this to work... so an email that said, sorry, we noticed you tried to get a bundle (the log knows what I wanted, where I failed, so easy to generate), here is the link to click and complete your purchase. And here is 2 movies on us as an apology would have kept me as a customer, and got the additional $$$$ per month for the next couple of years. It would also lead to upsell on my phone package etc. Instead, I went to a completely different company, bought service from them. Lost money for company A, negative sentiment for company A and me telling this story at the water cooler so I'm influencing more people to think negatively about company A. All in all, a loss of easy money, a ding in sentiment and image where a relatively simple solution exists and can be in place on the software I describe routinely in this blog... For those who are coming to Openworld and maybe see value in solving the above, or are thinking of how to solve this, come visit us in Moscone North - Oracle Red Lounge or in the Engineered Systems Showcase.

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  • Globacom and mCentric Deploy BDA and NoSQL Database to analyze network traffic 40x faster

    - by Jean-Pierre Dijcks
    In a fast evolving market, speed is of the essence. mCentric and Globacom leveraged Big Data Appliance, Oracle NoSQL Database to save over 35,000 Call-Processing minutes daily and analyze network traffic 40x faster.  Here are some highlights from the profile: Why Oracle “Oracle Big Data Appliance works well for very large amounts of structured and unstructured data. It is the most agile events-storage system for our collect-it-now and analyze-it-later set of business requirements. Moreover, choosing a prebuilt solution drastically reduced implementation time. We got the big data benefits without needing to assemble and tune a custom-built system, and without the hidden costs required to maintain a large number of servers in our data center. A single support license covers both the hardware and the integrated software, and we have one central point of contact for support,” said Sanjib Roy, CTO, Globacom. Implementation Process It took only five days for Oracle partner mCentric to deploy Oracle Big Data Appliance, perform the software install and configuration, certification, and resiliency testing. The entire process—from site planning to phase-I, go-live—was executed in just over ten weeks, well ahead of the four months allocated to complete the project. Oracle partner mCentric leveraged Oracle Advanced Customer Support Services’ implementation methodology to ensure configurations are tailored for peak performance, all patches are applied, and software and communications are consistently tested using proven methodologies and best practices. Read the entire profile here.

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  • Two interesting big data sessions around Openworld

    - by Jean-Pierre Dijcks
    For those who want to talk (not listen) about big data, here are 2 very cool sessions: BOF9877 - A birds of a feather session around all things big data. It is on Monday, Oct 1, 6:15 PM - 7:00 PM - Marriott Marquis - Golden Gate. While all guests on the panel are special, we will have very special guest on the panel. He is a proud owner of a Big Data Appliance (see here). Then there is a Big Data SIG meeting (the invite from Gwen): I'd like to invite everyone to our OOW12 meet up. We'll meet on Tuesday, October 2nd, 8:45 to 9:45 at Moscone West Level 3, Overlook 3. We will network, socialize and discuss plans for the group. Which topics interest us for webinars? Which conferences do we want to meet in? What other activities we are interested in? We can also discuss big data topics, show off our great work, and seek advice on the challenges. Other than figuring out what we are collectively interested in, the discussion will be pretty open. Here is the official invite. See you at Openworld!!

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  • Speak now! Call for Papers at Oracle Openworld is now open

    - by Jean-Pierre Dijcks
    Present Your Thoughts to Thousands of Oracle Customers, Developers, and Partners Do you have an idea that could improve best practices? A real-world experience that could shed new light on IT? The Oracle OpenWorld call for papers is now open. This is your opportunity to speak your mind to the world’s largest gathering of the most-knowledgeable IT decision-makers, leading-edge developers, and advanced technologists. So take a look at our criteria and join us at Oracle OpenWorld. We look forward to hearing from you. Register Early and Save See and learn about the newest products. Meet experts and business leaders. All for less. Register for Oracle OpenWorld before March 30, 2012, and you’ll save up to US$800 off the registration. Register now.

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  • Call for Paper: Oracle OpenWorld 2011

    - by jean-pierre.dijcks
    OpenWorld 2011 is now open for the public to submit session proposals. We would like to encourage our customers, and partners to participate in this ‘call for papers” (CFP) process. CFP for the general public, non-Oracle employee submitters, closes on March 27, 2011. Here are the details: Conference Location: Moscone Convention Center, San Francisco, CA. Conference Date: Sunday - Thursday, October 2 - 6, 2011 Conference Website: http://www.oracle.com/us/openworld CFP Website: https://oracleus.wingateweb.com/portal/cfp/ Paper submission key dates: Deliverables Due Dates Call for Papers Begins Wednesday, March 9 Call for Papers Ends Sunday, March 27 – 11:59 pm PDT Notifications for Accepted and Declined Submissions Sent End of May Questions regarding the Call for Papers, send an email to [email protected]

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  • Read how a customer uses Oracle NoSQL Database

    - by Jean-Pierre Dijcks
    For those who have had the pleasure to be in SF for Oracle Openworld, you might have seen or heard about this story already. If you did not, here is a great story on how to use Oracle NoSQL Database. Apart from all the cool technology, I'm just excited that this is a company founded by a football international and dealing with sports data, games and other cool things. Like an all things cool combo in one place.

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  • Using R on your Oracle Data Warehouse

    - by jean-pierre.dijcks
    Since it is Predictive Analytics World in our backyard (or are we San Francisco’s backyard…?) I figured it is well worth the time to dust of some old but important news. With big data (should we start calling it “any data analytics” instead?) being the buzz word and analytics the key operative goal, not moving data around is becoming more and more critical to the business users. Why? Because instead of spending time on moving data around into your next analytics server you should be running analytics on those CPUs. You could always do this with Oracle Data Mining within the Oracle Database. But a lot of folks want to leverage R as their main tool. Well, this article describes how you can do this, since 2010… As Casimir Saternos concludes in the article; “There is a growing awareness of the need to effectively analyze astronomical amounts of data, much of which is stored in Oracle databases. Statistics and modeling techniques are used to improve a wide variety of business functions. ODM accessed using the R language increases the value of your data by uncovering additional information. RODM is a powerful tool to enable your organization to make predictions, classify data, and create visualizations that maximize effectiveness and efficiencies.” Happy Analysis!

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  • E-Book on big data (featuring Analysts, Customers and more)

    - by Jean-Pierre Dijcks
    As we are gearing up for Openworld, here is a nice E-book on big data to start paging through. It contains Gartner's take on big data, customer and partner interviews and a lot more good info. Enjoy the read so you come prepared for Openworld!! Read the E-Book here. For those coming to Oracle Openworld (or the Americas Cup races around the same time), you can find big data sessions via this URL. Enjoy!!

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  • Big Data Sessions at Openworld 2012

    - by Jean-Pierre Dijcks
    If you are coming to San Francisco, and you are interested in all the aspects to big data, this Focus On Big Data is a must have document.  Some (other) highlights: A performance demo of a full rack Big Data Appliance in the engineered systems showcase A set of handson labs on how to go from a NoSQL DB to an effective analytics play on big data Much, much more See you all in a few weeks in SF!

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  • [Toplink] How to add c3p0 or DBCP Connection pool?

    - by Jean N.T.
    Hello, Could you please explain to me how to add a standalone c3pO or DBCP connection pool to my toplink-based JPA project? I have a persistence.xml file, and everytime I want to query the database, I'm doing this: EntityManagerFactory emf = this.getEntityManagerFactory(); // Surely using persistence.xml to set up the factory EntityManager em = emf.createEntityManager(); ... Where do I build the bridge between my external connection pool manager and Toplink? Any ideas or links are welcomed. Regards, Jean

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