An Overview of DB2 Federation

DB2 Federation
DB2 Federation

Data analytics has changed where data is no longer manageable in relational databases only. Data is flowing from various sources which are not of the same format. This means it is not possible to store all data in the same repository. Some are best suited for storing in relational databases, others for Apache Hadoop while others are best suited for NoSQL databases.

During data analyzing, so much time is taken in trying to bring the distributed data together instead of obtaining insights. Db2 Federation has come to the rescue of data analysts. Federation concept in db2 eliminates the need for storing data in different repositories and reduces the hassle of getting insights.

What is DB2 Federation?

DB2 federation is a data integration technology that permits remote database objects to be accessed as local DB2 database objects. This technology connects multiple databases and makes them appear like one database.

How does DB2 federation work?

Federation allows you to access all of your data that is on multiple distributed databases using a single query. When implemented in an organization, this technology can be used to access data that is on any of the organization’s Db2, whether local or in the cloud.

Why use DB2 federation?

So, why should you use the federation? This concept brings data of all formats into one virtual source. With data being retrieved from one virtual source, analyzing it becomes cost-effective and efficient.

What are its primary use cases for DB2 federation?

Merging of various sources of data

DB2 federation facilitates consolidating of data from sources data local and cloud to form one virtual data source. This eliminates the process of migrating data which can be expensive and troublesome.

Increase the capacity of a repository beyond the fixed limits

Physical storage capacity is bound to have a limit which is one reason you may find an organization has distributed its data in various repositories. With federation, the storage is virtual and therefore doesn’t have any limit. This technology can greatly help you if your physical dataset is running low on space.

Linking up to Db2 Warehouse on Cloud

People who use Db2 products can federate data from Db2 on Cloud and Db2 Warehouse on the Cloud. This will give them a joint interface where they can access, add, query, and analyze data without encountering the complex ETL processes. Better still, no additional code will be required to execute all these processes. This makes it easy for people with the low technical know-how to use these products smoothly.

Split data across different servers

At times, you might choose to partition your data. With federation integration technology, partitioned data can be queried with a unified interface. Federation allows you to better balance your workloads, scale precise parts of an app, and create micro-services that work harmoniously.

Generally, db2 federation makes it access data by bringing it together into a single virtual source. This brings about cost and time-saving benefits. When you want to analyze data, you can get insights immediately instead of spending a lot of time querying through repositories.

Related References

OLTP vs Data Warehousing

OLTP Versus Data Warehousing

I’ve tried to explain the difference between OLTP systems and a Data Warehouse to my managers many times, as I’ve worked at a hospital as a Data Warehouse Manager/data analyst for many years. Why was the list that came from the operational applications different than the one that came from the Data Warehouse? Why couldn’t I just get a list of patients that were laying in the hospital right now from the Data Warehouse? So I explained, and explained again, and explained to another manager, and another. You get the picture.
In this article I will explain this very same thing to you. So you know  how to explain this to your manager. Or, if you are a manager, you might understand what your data analyst can and cannot give you.

OLTP

OLTP stands for OLine Transactional Processing. With other words: getting your data directly from the operational systems to make reports. An operational system is a system that is used for the day to day processes.
For example: When a patient checks in, his or her information gets entered into a Patient Information System. The doctor put scheduled tests, a diagnoses and a treatment plan in there as well. Doctors, nurses and other people working with patients use this system on a daily basis to enter and get detailed information on their patients.
The way the data is stored within operational systems is so the data can be used efficiently by the people working directly on the product, or with the patient in this case.

Data Warehousing

A Data Warehouse is a big database that fills itself with data from operational systems. It is used solely for reporting and analytical purposes. No one uses this data for day to day operations. The beauty of a Data Warehouse is, among others, that you can combine the data from the different operational systems. You can actually combine the number of patients in a department with the number of nurses for example. You can see how far a doctor is behind schedule and find the cause of that by looking at the patients. Does he run late with elderly patients? Is there a particular diagnoses that takes more time? Or does he just oversleep a lot? You can use this information to look at the past, see trends, so you can plan for the future.

The difference between OLTP and Data Warehousing

This is how a Data Warehouse works:

The data gets entered into the operational systems. Then the ETL processes Extract this data from these systems, Transforms the data so it will fit neatly into the Data Warehouse, and then Loads it into the Data Warehouse. After that reports are formed with a reporting tool, from the data that lies in the Data Warehouse.

This is how OLTP works:

Reports are directly made from the data inside the database of the operational systems. Some operational systems come with their own reporting tool, but you can always use a standalone reporting tool to make reports form the operational databases.

Pro’s and Con’s

Data Warehousing

Pro’s:

  • There is no strain on the operational systems during business hours
    • As you can schedule the ETL processes to run during the hours the least amount of people are using the operational system, you won’t disturb the operational processes. And when you need to run a large query, the operational systems won’t be affected, as you are working directly on the Data Warehouse database.
  • Data from different systems can be combined
    • It is possible to combine finance and productivity data for example. As the ETL process transforms the data so it can be combined.
  • Data is optimized for making queries and reports
    • You use different data in reports than you use on a day to day base. A Data Warehouse is built for this. For instance: most Data Warehouses have a separate date table where the weekday, day, month and year is saved. You can make a query to derive the weekday from a date, but that takes processing time. By using a separate table like this you’ll save time and decrease the strain on the database.
  • Data is saved longer than in the source systems
    • The source systems need to have their old records deleted when they are no longer used in the day to day operations. So they get deleted to gain performance.

Con’s:

  • You always look at the past
    • A Data Warehouse is updated once a night, or even just once a week. That means that you never have the latest data. Staying with the hospital example: you never knew how many patients are in the hospital are right now. Or what surgeon didn’t show up on time this morning.
  • You don’t have all the data
    • A Data Warehouse is built for discovering trends, showing the big picture. The little details, the ones not used in trends, get discarded during the ETL process.
  • Data isn’t the same as the data in the source systems
    • Because the data is older than those of the source systems it will always be a little different. But also because of the Transformation step in the ETL process, data will be a little different. It doesn’t mean one or the other is wrong. It’s just a different way of looking at the data. For example: the Data Warehouse at the hospital excluded all transactions that were marked as cancelled. If you try to get the same reports from both systems, and don’t exclude the cancelled transactions in the source system, you’ll get different results.

online transactional processing (OLTP)

Pro’s

  • You get real time data
    • If someone is entering a new record now, you’ll see it right away in your report. No delays.
  • You’ve got all the details
    • You have access to all the details that the employees have entered into the system. No grouping, no skipping records, just all the raw data that’s available.

Con’s

  • You are putting strain on an application during business hours.
    • When you are making a large query, you can take processing space that would otherwise be available to the people that need to work with this system for their day to day operations. And if you make an error, by for instance forgetting to put a date filter on your query, you could even bring the system down so no one can use it anymore.
  • You can’t compare the data with data from other sources.
    • Even when the systems are similar. Like an HR system and a payroll system that use each other to work. Data is always going to be different because it is granulated on a different level, or not all data is relevant for both systems.
  • You don’t have access to old data
    • To keep the applications at peak performance, old data, that’s irrelevant to day to day operations is deleted.
  • Data is optimized to suit day to day operations
    • And not for report making. This means you’ll have to get creative with your queries to get the data you need.

So what method should you use?

That all depends on what you need at that moment. If you need detailed information about things that are happening now, you should use OLTP.
If you are looking for trends, or insights on a higher level, you should use a Data Warehouse.

 Related References

Databases – Database Isolation Level Cross Reference

Database Type Isolation Levels Cross Reference
Database And Tables

 

Here is a table quick reference of some common database and/or connection types, which use connection level isolation and the equivalent isolation levels. This quick reference may prove useful as a job aid reference, when working with and making decisions about isolation level usage.

Database isolation levels

Data sources

Most restrictive isolation level

More restrictive isolation level

Less restrictive isolation level

Least restrictive isolation level

Amazon SimpleDB

Serializable Repeatable read Read committed Read Uncommitted

dashDB

Repeatable read Read stability Cursor stability Uncommitted read

DB2® family of products

Repeatable read Read stability* Cursor stability Uncommitted read

Informix®

Repeatable read Repeatable read Cursor stability Dirty read

JDBC

Serializable Repeatable read Read committed Read Uncommitted

MariaDB

Serializable Repeatable read Read committed Read Uncommitted

Microsoft SQL Server

Serializable Repeatable read Read committed Read Uncommitted

MySQL

Serializable Repeatable read Read committed Read Uncommitted

ODBC

Serializable Repeatable read Read committed Read Uncommitted

Oracle

Serializable Serializable Read committed Read committed

PostgreSQL

Serializable Repeatable read Read committed Read committed

Sybase

Level 3 Level 3 Level 1 Level 0

 

Related References

 

Netezza / PureData – What client tools to use with Netezza

Occasionally, a client will want a list of tools to work with Netezza / PureData, other than the Netezza Administrator Client.  Honestly, there are several tools which could be used, if they have odbc and/or jdbc connectivity.  However, these are the tools which keep being used across different customers.

Open Source

For customers willing to work with an open-source tool Aginity for Netezza provides a significant set of capabilities, including script generation, which can be a significant productivity accelerator for development and operations support teams.

Aginity for Netezza

Free From IBM

For customer who does not want to work with open-source tools, then IBM Data studio is an acceptable tool and has the added benefit of being free from IBM.

IBM Data Studio

IBM Software > Products > Data management platform > Data management > IBM Data Studio

Related References

Where to download IBM Data Studio?

IBM data studio is offered free from IBM, and can be helpful when working with DB2 and Puredata/Netezza using a JDBC driver.

What you need to DownLoad IBM Data Studio

  • You will need an IBM ID and password

Basic down load steps

  • Enter your IBM ID, and password, then click ‘sign in’.
  • On the IBM Data Studio Client, license page, check ‘I agree’ and then click ‘I confirm’
  • On the IBM Data Studio Client, download page, Select the desired method tab, Then
    • Select the desired product or products and click ‘Download now.

Related References

IBM Data Studio

IBM Software > Products > Data management platform > Data management > IBM Data Studio

IBM Data Studio Client (Download)

IBM Support

Download and install IBM Data Studio Version 4.1.x

IBM Support

System requirements for IBM Data Studio Version 4.1.x

IBM Knowledge Center

Data Studio, Data Studio 4.1.1, Overview, Overview of IBM Data Studio

Netezza / PureData – How to convert an epoch field to a timestamp

This is one of the items, which we need to do in Netezza SQL from time to time, but is not exactly obvious to the average SQL user.  So, having a pattern for making the conversion for an epoch field to a timestamp can be a real time saver.

Epoch Field Conversion to A Timestamp Format

to_timestamp(‘1970-01-01 00:00:00′,’YYYY-MM-DD HH24:MI:SS’) + (<> * interval ‘.000001 seconds’) as Entry_TS_Timestamp_Format

Epoch Timestamp Conversion Example SQL 1

select

entry_ts as Orginial_Format_Entry_TS,

to_timestamp(‘1970-01-01 00:00:00′,’YYYY-MM-DD HH24:MI:SS’) + (entry_ts * interval ‘.000001 seconds’) as Entry_TS_Timestamp_Format

from _v_system_util

limit 1;

Epoch Timestamp Conversion Example SQL 2

select

entry_ts as Orginial_Format_Entry_TS,

to_date(‘1/1/1970′,’MM/DD/YYYY’) + (entry_ts * interval ‘.000001 seconds’) as Entry_TS_Timestamp_Format

from _v_system_util

limit 1;

Related References