Is Software configuration management helpful in your work?

http://www.poll-maker.com/poll503229x2D7d5a0B-21

Basic Memory Structures

Refer: http://docs.oracle.com/cd/E11882_01/server.112/e40540/memory.htm#CNCPT7778

The basic memory structures associated with Oracle Database include:

• System global area (SGA)
  The SGA is a group of shared memory structures, known as SGA components, that contain data and control information for one Oracle Database instance. The SGA is shared by all server and background processes. Examples of data stored in the SGA include cached data blocks and shared SQL areas.
• Program global area (PGA)
  A PGA is a nonshared memory region that contains data and control information exclusively for use by an Oracle process. The PGA is created by Oracle Database when an Oracle process is started.
  One PGA exists for each server process and background process. The collection of individual PGAs is the total instance PGA, or instance PGA. Database initialization parameters set the size of the instance PGA, not individual PGAs.
• User Global Area (UGA)
  The UGA is memory associated with a user session.
• Software code areas
  Software code areas are portions of memory used to store code that is being run or can be run. Oracle Database code is stored in a software area that is typically at a different location from user programs—a more exclusive or protected location.

Figure 14-1 illustrates the relationships among these memory structures.

Figure 14-1 Oracle Database Memory Structures

Description of "Figure 14-1 Oracle Database Memory Structures"

Oracle Database Memory Management

Memory management involves maintaining optimal sizes for the Oracle instance memory structures as demands on the database change. Oracle Database manages memory based on the settings of memory-related initialization parameters. The basic options for memory management are as follows:

• Automatic memory management
  You specify the target size for instance memory. The database instance automatically tunes to the target memory size, redistributing memory as needed between the SGA and the instance PGA.
• Automatic shared memory management
  This management mode is partially automated. You set a target size for the SGA and then have the option of setting an aggregate target size for the PGA or managing PGA work areas individually.
• Manual memory management
  Instead of setting the total memory size, you set many initialization parameters to manage components of the SGA and instance PGA individually.

How Devops can be improved?

Few tips to improve Devops:

Frequency of deployments – The number should either remain steady or go up week to week.
Volume of changes – Measure the volume and complexity of user stories and new lines of code deployed.
Amount of time from development to deployment – The lead time from when the code begins development till it’s deployed to production. The amount of time should decline as the team develops – and is a central gauge of how efficient the process is and where it must be improved.
What is the ratio of unsuccessful deployments? – How often do deployments fail or even cause outages? As DevOps is implemented, the quality of the deployments should go up and the percentage of failed deployments should go down.
Recovery Time – This is perhaps the greatest test of the quality of the team – how long does it take to recover when a failure does occur. Although the time should generally trend downwards, teams should not be discouraged by the occasional spike as they run into issues for the first time.
Customer Tickets – The goal of DevOps is to increase deployments without causing failures. By reviewing the number of customer tickets you’ll have a good idea how well you’re doing.
Increase in user volume – By tracking how many users are signing up you can make sure the organization can handle the new requests.
Response Time – This number should remain steady no matter the percentages of change in user volume as the product should be functioning in predetermined thresholds.

Oracle Basic : Oracle 10g vs Oracle 11g

Top few features to list - Difference between 10g and 11g

1.Simplified and improved automatic memory management

2.New fault diagnosability infrastructure to prevent, detect, diagnose, and help resolve critical database errors

3.Invisible Indexes

4.Virtual columns

5.Enhanced security for password-based authentication by enabling use of mixed case in passwords.

6.Tablespace-level encryption

7.Ability to online redefine tables that have materialized view logs

Difference between 11gr1 and 11gr2

Oracle 11gR2 is the second and terminal release of the Oracle 11g database.
The common theme for this release is "Consolidate. Compress. Control.".

New Features:

1. Oracle RAC One Node
2. Improved data compression ratios (up to 20x).
3. Ability to upgrade database applications while users remain online.
4. New ease-of-use features that make Grid computing more accessible.
5. deferred segment creation new feature in Oracle 11gR2
6. In-Memory Parallel Execution
7. Auto Degree of Parallelism (Auto DOP) and Parallel Statement Queuing
8. Significant Performance Improvement of MV On-Commit Fast Refresh
9. Database Resource Manager Instance Caging
10. Hybrid Columnar Compression

(source - internet)

How to get last runned cron job details in linux?

/var/log/cron contains cron job logs. But you need a root privilege to see.

Big Data!

Big data is nothing but an assortment of such huge and complex data that becomes very tedious to capture, store, process, retrieve and analyze it. Thanks to on-hand database management tools or traditional data processing techniques, things have become easier now. In fact, the concept of “BIG DATA” may vary from company to company depending upon its size, capacity, competence, human resource, techniques and so on. For some companies it may be a cumbersome job to manage a few gigabytes and for others it may be some terabytes creating a hassle in the entire organization.

The Four V’s Of Big Data

1. Volume: BIG DATA is clearly determined by its volume. It could amount to hundreds of terabytes or even petabytes of information. For instance, 15 terabytes of Facebook posts or 400 billion annual medical records could mean Big Data!

2. Velocity: Velocity means the rate at which data is flowing in the companies. Big data requires fast processing. Time factor plays a very crucial role in several organizations. For instance, processing 2 million records at share market or evaluating results of millions of students applied for competitive exams could mean Big Data!

3. Variety: Big Data may not belong to a specific format. It could be in any form such as structured, unstructured, text, images, audio, video, log files, emails, simulations, 3D models, etc. New research shows that a substantial amount of an organization’s data is not numeric; however, such data is equally important for decision-making process. So, organizations need to think beyond stock records, documents, personnel files, finances, etc. 

4. Veracity: Veracity refers to the uncertainty of data available. Data available can sometimes get messy and maybe difficult to trust. With many forms of big data, quality and accuracy are difficult to control like the Twitter posts with hash tags, abbreviations, typos and colloquial speech. But big data and analytics technology now permits to work with these types of data. The volumes often make up for the lack of quality or accuracy. Due to uncertainty of data, 1 in 3 business leaders don’t trust the information they use to make decisions.

Why Big Data analysis is crucial:

1. Just like labor and capital, data has become one of the factors of production in almost all the industries.

2. Big data can unveil some really useful and crucial information which can change decision making process entirely to a more fruitful one.

3. Big data makes customer segmentation easier and more visible, enabling the companies to focus on more profitable and loyal customers.

4. Big data can be an important criterion to decide upon the next line of products and services required by the future customers. Thus, companies can follow proactive approach at every step.

5. The way in which big data is explored and used can directly impact the growth and development of the organizations and give a tough competition to others in the row! Data driven strategies are soon becoming the latest trend at the Management level

Why Hadoop?

Hadoop can be contagious. It’s implementation in one organization can lead to another one elsewhere. Thanks to Hadoop being robust and cost-effective, handling humongous data seems much easier now. The ability to include HIVE in an EMR workflow is yet another awesome point. It’s incredibly easy to boot up a cluster, install HIVE, and be doing simple SQL analytics in no time. Let’s take a look at why Hadoop can be so incredible.

Key features that answer – Why Hadoop?

1. Flexible:

As it is a known fact that only 20% of data in organizations is structured, and the rest is all unstructured, it is very crucial to manage unstructured data which goes unattended. Hadoop manages different types of Big Data, whether structured or unstructured, encoded or formatted, or any other type of data and makes it useful for decision making process. Moreover, Hadoop is simple, relevant and schema-less! Though Hadoop generally supports Java Programming, any programming language can be used in Hadoop with the help of the MapReduce technique. Though Hadoop works best on Windows and Linux, it can also work on other operating systems like BSD and OS X.

2.  Scalable

Hadoop is a scalable platform, in the sense that new nodes can be easily added in the system as and when required without altering the data formats, how data is loaded, how programs are written, or even without modifying the existing applications. Hadoop is an open source platform and runs on industry-standard hardware. Moreover, Hadoop is also fault tolerant – this means, even if a node gets lost or goes out of service, the system automatically reallocates work to another location of the data and continues processing as if nothing had happened!

3. Building more efficient data economy:

Hadoop has revolutionized the processing and analysis of big data world across. Till now, organizations were worrying about how to manage the non-stop data overflowing in their systems. Hadoop is more like a “Dam”, which is harnessing the flow of unlimited amount of data and generating a lot of power in the form of relevant information. Hadoop has changed the economics of storing and evaluating data entirely!

4. Robust Ecosystem:

Hadoop has a very robust and a rich ecosystem that is well suited to meet the analytical needs of developers, web start-ups and other organizations. Hadoop Ecosystem consists of various related projects such as MapReduce, Hive, HBase, Zookeeper, HCatalog, Apache Pig, which make Hadoop very competent to deliver a broad spectrum of services.

5. Hadoop is getting more “Real-Time”!

Did you ever wonder how to stream information into a cluster and analyze it in real time? Hadoop has the answer for it. Yes, Hadoop’s competencies are getting more and more real-time. Hadoop also provides a standard approach to a wide set of APIs for big data analytics comprising MapReduce, query languages and database access, and so on.

6. Cost Effective:

Loaded with such great features, the icing on the cake is that Hadoop generates cost benefits by bringing massively parallel computing to commodity servers, resulting in a substantial reduction in the cost per terabyte of storage, which in turn makes it reasonable to model all your data. The basic idea behind Hadoop is to perform cost-effective data analysis present across world wide web!

7.  Upcoming Technologies using Hadoop:

With reinforcing its capabilities, Hadoop is leading to phenomenal technical advancements. For instance, HBase will soon become a vital Platform for Blob Stores (Binary Large Objects) and for Lightweight OLTP (Online Transaction Processing). Hadoop has also begun serving as a strong foundation for new-school graph and NoSQL databases, and better versions of relational databases.

8.  Hadoop is getting cloudy!

Hadoop is getting cloudier! In fact, cloud computing and Hadoop are synchronizing in several organizations to manage Big Data. Hadoop will become one of the most required apps for cloud computing. This is evident from the number of Hadoop clusters offered by cloud vendors in various businesses. Thus, Hadoop will reside in the cloud soon!