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Planet DB2 is an aggregator of blogs about the IBM DB2 database server. We combine and republish posts by bloggers around the world. Email us to have your blog included.

 

June 24, 2016


DB2Night Replays

The DB2Night Show #Z71: DB2 12 Preview

Presented by: Jeff Josten IBM Distinguished Engineer, DB2 z/OS Development "The DB2Night Show #Z71: DB2 12 Preview" Replays available in WMV and M4V formats! 98% of our studio audience learned something!Jeff provided a concise, clear overview of the new features in DB2 12 for z/OS. Watch the replay...

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June 21, 2016

Big Data University

This Week in Data Science (June 21, 2016)

Here’s this week’s news in Data Science and Big Data. Smart Toothbrush

Don’t forget to subscribe if you find this useful!

Interesting Data Science Articles and News

Upcoming Data Science Events

Cool Data Science Videos

The post This Week in Data Science (June 21, 2016) appeared first on Big Data University.


Kim May

IBM Q Replication: Frank’s Field Notes, Summer ‘16

Please join us Thursday, July 28th, from 11am-12pm EDT for a free webinar with an update on IBM’s Q Replication. Having worked with Q Replication customers since 2006, DB2 Gold Consultant and...

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DB2utor

Really, We're All Experts Here

Since joining IBM last year I've been spending more time speaking to user groups. This got me thinking about a post I wrote back in December 2012 on the professional benefits of public speaking. While I'd love for you to read the whole thing, I'll sum it up in four words: You can do this.
 

June 19, 2016


DB2Night Replays

The DB2Night Show #181: DB2 LUW Advanced Performance Diagnostics for SQL

@DavidKalmuk Special Guest: David Kalmuk, STSM, IBM Canada DB2 LUW Advanced Performance Diagnostics for SQL 100% of our audience learned something! David gave us another great IDUG presentation packed with tips and advice for monitoring and diagnosing performance issues in DB2 LUW. A PDF handout is available so you can copy/paste from his SQL examples! Watch and learn...

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DB2Night Replays

The DB2Night Show #180:DB2 LUW DPF/MPP for Single Partition DBAs in 10 Tips

@pkristipati Special Guest: Pavan Kristipati, Sr. DBA, Huntington Bank DB2 LUW DPF/MPP for Single Partition DBAs in 10 Tips! 100% of our audience learned something! During today's show, Pavan shares his excellent IDUG presentation in which he helps DBAs familiar with single partition databases understand how DPF works, how DPF is different, and he gives valuable administration advice! A PDF handout is available so you can copy/paste...

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June 16, 2016


Kim May

How Succeed with #IBM in 2016 – Ideas???

Working for an IBM Business Partner organization is not easy and seems to be getting harder each year. Factor in IBM’s continuing (or accelerating?) decline in sales, and figuring out how to be a...

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Henrik Loeser

Now available: DB2 Version 11 for Linux, UNIX, and Windows

The new version 11.1 of DB2 for Linux, UNIX, and Windows (DB2 LUW) is now available. Enjoy many product improvements for analytic and OLTP scenarios. Here is how to get started: This DB2 support...

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June 15, 2016


Dave Beulke

More DB2 Family Security Best Practices Part 8: z/OS Communications Server

This next part (Part 8) builds on the other DB2 Family Security Best Practices blogs which I wrote a while back. (The links for the other seven DB2 Security blogs can be found at the bottom of the page.) This blog talks about adding elements of DB2 security by layering in a z/OS Communication...

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DB2Night Show News

Grand Finale Climax! The DB2Night Show Season #7 has 3 more shows!

Free DB2 LUW Education, plus Free Starbucks coffee too! Friday 17 June 2016 is a big day featuring THREE DB2 LUW education events: 10:00am CDT: DB2 LUW Advanced Performance Diagnostics for SQL with...

...

DB2 Guys

The value of common database tools and linked processes for Db2, DevOps, and Cloud

by Michael Connor, Analytics Offering Management Today we released DB2 V11 for Linux, UNIX and Windows. The release includes updates to Data Server Manager (DSM) V2.1 and Data Server Driver connectivity V11 and Advanced Recovery Feature (ARF) V11.    As many of you may be aware of – 2 years ago we embarked on a strategy […]

Leons Petrazickis

Enable better git diffs on the Mac

git 2.9 brings new features that make reviewing changes easier. Everyone should set these configuration options to enable better git diffs. The post Enable better git diffs on the Mac appeared first...

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June 14, 2016

Big Data University

This Week in Data Science (June 14, 2016)

Here’s this week’s news in Data Science and Big Data. Big Data and Love

Don’t forget to subscribe if you find this useful!

Interesting Data Science Articles and News

Upcoming Data Science Events

The post This Week in Data Science (June 14, 2016) appeared first on Big Data University.


Craig Mullins

Four Important Buffer Pool Tuning Knobs in DB2 for z/OS

DB2 has five (well, four current) primary adjustable thresholds that can be modified using the ALTER BUFFERPOOL command.   These thresholds are as follows: The Sequential Steal Threshold, or VPSEQT, is the percentage of the buffer pool that can be occupied by sequentially accessed pages. For example, at the default value of 80, when this threshold is reached, 80% of the buffer pool...

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DB2 Guys

Migrating a DB2 database from a Big Endian environment to a Little Endian environment

By Roger Sanders, DB2 for LUW Offering Manager, IBM What Is Big-Endian and Little-Endian? Big-endian and little-endian are terms that are used to describe the order in which a sequence of bytes are stored in computer memory, and if desired, are written to disk. (Interestingly, the terms come from Jonathan Swift’s Gulliver’s Travels where the […]

DB2utor

developerWorks Community for DB2 Analytics Accelerator

Last year I noted that IBM was accelerating development of the DB2 Analytics Accelerator. With new maintenance releases coming out about every 3-6 months, it's a challenge to keep up with all the changes. That's why I was excited to discover the IBM developerWorks Community for DB2 Analytics Accelerator.
 

June 10, 2016


Henrik Loeser

Learn DB2 and dashDB with Stack Overflow

Top-voted DB2 questions on Stack Overflow When I want to learn more about the ins and outs of DB2 or dashDB or when I have some spare time and want some fun sharing my knowledge I visit Stack...

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June 07, 2016

Big Data University

This Week in Data Science (June 07, 2016)

Here’s this week’s news on Big Data. AI Teaching Assistant

Don’t forget to subscribe if you find this useful!

Interesting Data Science Articles and News

Upcoming Data Science Events

The post This Week in Data Science (June 07, 2016) appeared first on Big Data University.

Jack Vamvas

How to create a copy of a DB2 database table

Question: I’m making a change to DB2 LUW database table – and would like to first backup the table – before I make the change. What is a method for backing up a table and data?

 

Answer: This method will create the table LIKE the source table , and then the INSERT statement will take all the data from the source table and INSERT into the target table.

 

 

CREATE TABLE MYSCHEMA.A_NEW_TBL LIKE MYSCHEMA.AN_OLD_TBL;
INSERT INTO MYSCHEMA.A_NEW_TBL (SELECT * FROM MYSCHEMA.AN_OLD_TBL);

 

Note: Using this method will not copy across all objects associated with this table. The LIKE method copies the implicit definition of the table. The implicit definition does not include unique constraints, foreign key constraints, triggers, or indexes.

To get full details of all the options available for the CREATE TABLE .. LIKE option have a read through the documentation.

As an alternative method, which gives you greater control you can look at the process to  create a DDL on an existing DB2 table (DBA DB2)

Read More on DB2 Object management

Extract DB2 create database ddl using db2look and -createdb switch ...

Compare DDL of 2 databases in DB2 (DBA DB2)

 


DB2utor

Plan Management Best Practices for Upgrades

Each new DB2 release brings enhancements to the SQL optimizer, which is designed to speed response times by improving the access path used to return qualified data to the application. However, these improvements can only be realized by first REBINDing the program package.

Leon Katsnelson

Getting information about DB2 tables

A reference to a post on the new ADMINTABINFO administrative view and the new ADMIN_GET_TAB_INFO_V95 table function. Both are new to DB2 v9.5 and both provide an incredible amount of information about DB2 tables.
 

June 06, 2016

Big Data University

Data Scientist vs Data Engineer, What’s the difference?

Co-authored by Saeed Aghabozorgi and Polong Lin.

Data Scientists and Data Engineers may be new job titles, but the core job roles have been around for a while. Traditionally, anyone who analyzed data would be called a “data analyst” and anyone who created backend platforms to support data analysis would be a “Business Intelligence (BI) Developer”.

With the emergence of big data, new roles began popping up in corporations and research centers — namely, Data Scientists and Data Engineers.

Here’s an overview of the roles of the Data Analyst, BI Developer, Data Scientist and Data Engineer.

Data Analyst

Data Analysts are experienced data professionals in their organization who can query and process data, provide reports, summarize and visualize data. They have a strong understanding of how to leverage existing tools and methods to solve a problem, and help people from across the company understand specific queries with ad-hoc reports and charts.

However, they are not expected to deal with analyzing big data, nor are they typically expected to have the mathematical or research background to develop new algorithms for specific problems.

Skills and Tools: Data Analysts need to have a baseline understanding of some core skills: statistics, data munging, data visualization, exploratory data analysis, Microsoft Excel, SPSS, SPSS Modeler, SAS, SAS Miner, SQL, Microsoft Access, Tableau, SSAS.

Business Intelligence Developers

Business Intelligence Developers are data experts that interact more closely with internal stakeholders to understand the reporting needs, and then to collect requirements, design, and build BI and reporting solutions for the company. They have to design, develop and support new and existing data warehouses, ETL packages, cubes, dashboards and analytical reports.

Additionally, they work with databases, both relational and multidimensional, and should have great SQL development skills to integrate data from different resources. They use all of these skills to meet the enterprise-wide self-service needs. BI Developers are typically not expected to perform data analyses.

Skills and tools: ETL, developing reports, OLAP, cubes, web intelligence, business objects design, Tableau, dashboard tools, SQL, SSAS, SSIS.

Data Engineer

Data Engineers are the data professionals who prepare the “big data” infrastructure to be analyzed by Data Scientists. They are software engineers who design, build, integrate data from various resources, and manage big data. Then, they write complex queries on that, make sure it is easily accessible, works smoothly, and their goal is optimizing the performance of their company’s big data ecosystem.

They might also run some ETL (Extract, Transform and Load) on top of big datasets and create big data warehouses that can be used for reporting or analysis by data scientists. Beyond that, because Data Engineers focus more on the design and architecture, they are typically not expected to know any machine learning or analytics for big data.

Skills and tools: Hadoop, MapReduce, Hive, Pig, MySQL, MongoDB, Cassandra, Data streaming, NoSQL, SQL, programming.

Data Scientist

A data scientist is the alchemist of the 21st century: someone who can turn raw data into purified insights. Data scientists apply statistics, machine learning and analytic approaches to solve critical business problems. Their primary function is to help organizations turn their volumes of big data into valuable and actionable insights.

Indeed, data science is not necessarily a new field per se, but it can be considered as an advanced level of data analysis that is driven and automated by machine learning and computer science. In another word, in comparison with ‘data analysts’, in addition to data analytical skills, Data Scientists are expected to have strong programming skills, an ability to design new algorithms, handle big data, with some expertise in the domain knowledge.

Moreover, Data Scientists are also expected to interpret and eloquently deliver the results of their findings, by visualization techniques, building data science apps, or narrating interesting stories about the solutions to their data (business) problems.

The problem-solving skills of a data scientist requires an understanding of traditional and new data analysis methods to build statistical models or discover patterns in data. For example, creating a recommendation engine, predicting the stock market, diagnosing patients based on their similarity, or finding the patterns of fraudulent transactions.

Data Scientists may sometimes be presented with big data without a particular business problem in mind. In this case, the curious Data Scientist is expected to explore the data, come up with the right questions, and provide interesting findings! This is tricky because, in order to analyze the data, a strong Data Scientists should have a very broad knowledge of different techniques in machine learning, data mining, statistics and big data infrastructures.

They should have experience working with different datasets of different sizes and shapes, and be able to run his algorithms on large size data effectively and efficiently, which typically means staying up-to-date with all the latest cutting-edge technologies. This is why it is essential to know computer science fundamentals and programming, including experience with languages and database (big/small) technologies.

Skills and tools: Python, R, Scala, Apache Spark, Hadoop, data mining tools and algorithms, machine learning, statistics.

The post Data Scientist vs Data Engineer, What’s the difference? appeared first on Big Data University.


Omer Brandis

Temp tables - can't live without them, can never remember to plan for them in advance

how many of us remember to plan for them during the capacity planning phase and purchase enough storage ?
 

June 02, 2016


Philip Nelson

Database Geek Abroad : IDUG Berlin (2)

This blog post covers the first full day (Monday 5th November) of IDUG EMEA 2012 in Berlin.

 

The "official" day started with a keynote speech at 10 am. However the Content Committee had a meeting at 9 am to discuss what we plan to do in the year ahead (the first of several such meetings this week). If there is any particular DB2-related topic that you think IDUG should be covering then drop me a l

 

June 01, 2016


Dave Beulke

IDUG Conference Value

Another IDUG conference is complete, and the extensive conference information from application developers, DBAs, managers, IBMers, consultants and especially user speakers was as great as usual. The keynote, educational sessions, and presentations pointed out all the impressive DB2 z/OS, DB2 LUW,...

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Big Data University

This Week in Data Science (May 31, 2016)

Here’s this week’s news in Data Science and Big Data. Jacquard

Don’t forget to subscribe if you find this useful!

Interesting Data Science Articles and News

Upcoming Data Science Events

The post This Week in Data Science (May 31, 2016) appeared first on Big Data University.

 

May 31, 2016


Susan Visser

Refreshing

I've been encouraged to update my blog with activities that I'm currently involved with.  I'm sorry that I didn't make the time in the past year to keep this blog alive with fresh content.  Let's see if I can get my writing "mojo" back with content to please my readers.

 

Let me whet your appetite with what I have been doing for IBM in the past year.  I'm a social strategist for IBM Analytics Financial Solutions. That means that I have been writing, podcasting and creating content about IBM's Industry Solutions that are aimed at Banks, Insurance Companies, and Wealth Managers.  I've been involved with the Dave Haase story as well as looking at fraud solutions and more.

 

For the next while, I'll create blogs that step you though what this means to me and those of you reading this blog.

 

Susan


DB2utor

Setting Maximum Open Datasets to Greater Than 10,000

Recently I was sitting in on a customer review and the topic of maximum number of open data sets came up. In DB2 11, the maximum number of open datasets is increased from 10,000 to 200,000.

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planetDB2 is an aggregator of blogs about the IBM DB2 database server. We combine and republish posts by bloggers around the world. Email us to have your blog included.
 

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