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Join the DBA training in Pune to make your career in DBA

In today’s E-world, DBA makes ways to store the data in an organized way and manage everything digitally.

Oracle DBA will definitely hold importance as long as databases are there. But we need to keep developing ourself and be updated with the newest technology. If you have the ability to note down the data properly and strategise your work or data in a better way, then you are the best to become a database administrator.

There are many new evolving technologies in DBA like Oracle RAC, Oracle Exadata, Golden Gate, ADM, Oracle Cloud etc. These are new places that promise growth on which you can make money. These technologies are relatively new and experienced professionals are less, which helps create many job opportunities.

Know your field of interest and start developing your skillset for a promising career in the field of DBA.

DBA training in Pune is always there for you to provide the placement as a DBA professional and we at CRB Tech have the best training facilities. We will provide you the 100% placement guaranteed.

Thus, DBA training would be the best option for you to make your career in this field .

What can be the better place than CRB Tech for DBA training in Pune?

DBA institute in Pune will help in you in understanding the basic concepts of DBA related ideas and thus improve your skills in PL/SQL queries.

CRB Tech is the best institution for DBA in Pune.

There are many institutes which offer training out of which CRB Tech stands apart and is always the best because of its 100% guaranteed placements and sophisticated training.

Reason for the best training in CRB Tech:

This has a variety of features that ensure that is the best option from among other DBA programs performed at other DBA training institutions in Pune. These are as follows:

1. You will definitely be a job holder:

We provide a very high intensive training and we also provide lots of interview calls and we make sure that you get placed before or at the end of the training or even after the training and not all the institutes provide such guarantees.

2. What is our placement record?

Our candidates are successfully placed in IBM, Max secure, Mind gate, saturn Infotech and if you refer the statistics of the number of students placed it is 100%

3. Ocean of job opportunities

We have lots of connections with various MNCs and we will provide you life time support to build your career.

4.LOI (Letter of intent):

LOI is offered by the hiring company at the starting itself and it stands for Letter Of Intent and after getting that, you will get the job at the end of the training or even before the training ends.

5. Foreign Language training:

German language training will help you while getting a job overseas in a country like Germany.

6.Interview calls:

We provide unlimited interview calls until the candidate gets placed and even after he/she gets placed he/she can still seek help from us for better job offers. So dont hesitate to join the DBA training in Pune.

7.Company environment

We provide corporate oriented infrastructure and it is in such a way that the candidates in the training will actually be working on the real time projects. Thus it will be useful for the candidate once he/she get placed. We also provide sophisticated lab facilities with all the latest DBA related software installed.

8.Prime Focus on market based training:

The main focus over here is dependent on the current industry related environment. So we provide such training in your training days. So that it will be easier for you to join the DBA jobs.

9.Emphasis on technical knowledge:

To be a successful DBA, you should be well aware of all the technical stuffs and the various concepts of SQL programming and our DBA training institutes have very good faculties who teach you all the technical concepts

Duration and payment assistance:

The duration of the training at our DBA institution in Pune is for

4 months.

The DBA sessions in Pune run for 7-8 hours on Monday to Friday.

Talking about the financial options:

Loan options:

Loan and installment choices are made available for expenses of charges.

Credit Card:

Students can opt the option of EMI transaction on their bank cards.

Cash payment:

Fees can also be paid in cash choices.

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Google BigTable

For aiding applications, BigTable was designed and need massive scalability from its first iteration and with the help of petabytes of data the technology was intended to be used. On the clustered systems the database was designed and it uses a simple data model that has been described by Google as a persistent, sparse, distributed, multi-dimensional sorted map.

With the help of row key for the purpose of the indexing the map the data is assembled and with respect to row, column keys and timestamps data is assembled. Thus there is a high capacity achieved by compression algorithms.

Similar to Google App Engine Datastore, Google Earth, Google Personalized Search and Google Analytics; Google Bible serves as the database for applications. The software kept as the sole property and in-house technology as said by the Google. In a technical paper by Google software developers revealed Bigtable details presented at the USENIX Symposium on Operating Systems and Design Implementation in 2006.

Other open source development teams and organizations are permitted by the Google thorough description of Bigtable’s inner workings for the purpose of developing Big table along with Apache HBase database, which is supposed to run above the HDFS. There are other instances like Cassandra found at Facebook Inc, and an open source technology, and Hypertable that is sold in a commercial version as an aliter solution of HBase.

Here are the few things that are to be delivered by Cloud Bigtable for benefitting the organizations:

  • Unmatched Performance: single digit millisecond performance
  • Open Source interface: All of the big data that is existing is supported because it is accessed by HBase API and Google big data products are supported by the Hadoop ecosystem. With the help of easy ingestion tools import of data is possible.
  • Low Cost: ownership cost is reduced with the help of efficiency of Bigtable
  • Security: There are complete security and encryption of data in the cloud bigtable

For more information join the DBA Course to make your career in this field successfully.

Stay connected to CRB Tech for more technical optimization and other updates and information.

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Different Technologies Transforming The Database

Explain database? Earlier it was quite simple. All the data was put in tables of very linear columns with one row per entry. Therefore it lead to long rectangular information extending into the future. Bedrock of modern computing is nothing but relational database. Can you still find these wonderful new options? Does the data need to be fitted into some large matrix to be a database? There are some people who use the word data store to be away from the modern mechanisms due to the word database and it is very tightly connected to our minds of the old structure in tabular form. Here are four ways in reshaping the database:

1) GPU Computing

Earlier there were video cards used for expanding the scenes for kid’s games but currently, whatever is called as GPU is doing good with non-graphical processing. One of the best non-graphical working for them is to tackle and it can be searched through data. And why not? A parallel operation is made inherently by plowing endless piles of data with a parallel operation to make many rudimentary jobs repeated lots of times. If GPU memory is the best thing for data to fit then you can get it done without the index. If there is a quick change in data in a rapid way then the index is never used and losing the preprocessing can be very much effective.

2) Non-Volatile Memory (NVRAM)

Those ancient programmers made it easy. They were not in a position to juggle the data from RAM and the disk with detailed protocols for ensuring consistency. There was an iron core earlier back then and it was not removed when the power was off. There are some chip producers during good times that can come back and mention about replacing RAM with NVRAM or nonvolatile memory.

3) Geospatial Darabases

You can add a few extra functions with the help of geospatial databases that make sorting, searching, and intersect lots of easier in two-dimensional space. For instance, spatial indices lead to usual work with the addition of a grid above it to coordinate the space and make it run in rapidly for searching rows that are available in two dimensional and three-dimensional worlds.

4) Graph Databases

For an easier way of running graph databases make queries. You cannot find continuous fetching from tables due as the query understands how to look in the nearby is specified by links. You can make use of tools like Neo4J, Orient DB, and Data Stax for counting barely with your hands and feet. They possess their own query language.

Join the DBA course and know more about this topic and make your career in this field.

Stay connected to CRB Tech for more technical optimization and other updates and information.

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RethinkDB

For the real-time Web in an open source database, RethinkDB is used. For streaming live updates it has a built-in change notification system to your application. For the purpose of polling new data, you need to have the push of database that changes to you. For subscribing the streaming updates is the ability from the layer of persistence that can simplify your application and thereby make support clients easily for maintaining back-end persistent connections. A schemaless JSON document store is none other than RethinkDB but it also aids relational features similar to table joins. Clustering is supported by RethinkDB which makes it easy for scaling. Sharding can be configured and replicated for the cluster through the built-in database administrative web interface.

RethinkDB Software

On Mac OS X and Linux we can run RethinkDB and under active development, a native windows port is made but it is not present for download. How to install the database details can be got from RethinkDB documentation that is available online. Yum, and APT repositories are offered for users in Linux and a pkg installer for OS X. For the purpose of compiling the source code from GitHub, you can install the software RethinkDB with Docker. RethinkDB’s founder is none other than Slav Akhmechet and is a database company for specific developers to help them and construct real-time Web applications. He was a systems engineer prior to RethinkDB in the financial industry, operating on scaling custom database systems.

A brief introduction to ReQL

A RethinkDB query language called ReQL offers a massive and easier way to change JSON documents. A general introduction to ReQL concepts is found in the documents. It is not mandatory to read it to be productive with RethinkDB but if you read you will understand the concepts well.

Thus join the DBA course to know more about this topic.

Stay connected to CRB Tech for more technical optimization and other updates and information.

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6 Reasons That Proves Smart Service Is Required For Cloud Transformation

It is quite significant to understand the implications of making certain choices for being benefited from the cloud technology. For producing better outcome a smart service provider must be installed and it can avoid bad decisions. While transferring to the cloud might be easy and the actual execution can lead to serious headaches. The medicine you require for curing your migraine before it starts is done with the help of Smart Service.

Consider The Following Stuff Before You Execute Your Cloud Transformation:

1. For transferring your data and applications it might seem very attractive to a public cloud provider and you need to look at the consequences which are possible to occur. In return of convenience, you need to hand over the controls along with a public cloud provider. You have the benefits of the cloud along with your own private cloud while maintaining full control and ownership. A well-trained IT staff and a significant investment are required for doing this. Depending on your restrictions and requirements this solution is the best for you.

2. For planning and building, a private cloud consumes a lot of time. A review of the present data center is begun especially for revealing the kind of equipment that is actually re-used for the cloud. There is no need to touch legacy environment if you choose to construct entirely new cloud data centers.

3. After the determination of desired end state you may need to shift the data and applications to the new cloud environment and with minimal impact on zero data loss and productivity, this task must be accomplished. There are lots of preparations required for this and it can be quite complex. With the migration of applications, the data transfer needs to be aligned for avoiding synchronization problems.

4. For adding or removing resources the benefits of the cloud solution is dynamic. There are lots of ways to do this and it is called Advanced, Dynamic or user provisioning. In real time and fully automatic resources the dynamic provisioning is provided. In advance, the available resources are got by the users with respect to advanced provisioning. There are lots of solutions that has various consequences and a different price tag. A good understanding of pros and cons is required for making the right choice.

5. It takes a lot of time for a proper transition of cloud but for years of completion for a particular transition, it means high cost, frustration, and risk of project abandonment. Benefits are achieved more rapidly and disruptions end sooner.

6. For managing the cloud transition of project needs a financial investment for engaging a specialist firm which done using an internal staff is very much attractive. Instead of hiring an external provider why not use the resources that you are already paying for? The reason might be that there won’t be a cloud migration done before with the help of an internal staff.

Thus our DBA Course is more than enough for you to make your profession in this field.

Stay connected to CRB Tech for more technical optimization and other updates and information.

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Apache Spark Interview Questions and Answers

There are lots of candidates who are looking out for DBA jobs and for them this blog will help in providing some DBA interview questions to prepare and perform well in the interview and then make career in this field.

1) What is Apache Spark?

A flexible data processing framework which is easy to use is Spark and it is very fast. Cyclic data flow is supported with the help of advanced execution engine aiding data flow which is cyclic and in-memory computing. On Hadoop spark can run, independently or in the cloud and is very much capable of accessing diverse data sources including HDFS, HBase, Cassandra, and others.

2) Define RDD

Resilient Distribution Datasets is the full form for RDD and it is a fault tolerant collection of elements which are operational and they run parallel. It has a distributed and an immutable RDD which has a partitioned data. There are primarily two types of RDD:

Parallelized Collections: There is a parallel connection between RDDs which are in existence.

Hadoop datasets: In HDFS or other storage system, functions are performed on each file record.

3) Discuss the working of the Spark Engine

For the purpose of distributing, scheduling and monitoring Spark Engine are responsible across the cluster.

4) Explain Partitions

For the purpose of splitting or logical division of data similar to MapReduce, partitioning is done but it is quite smaller. For deriving logical units of data, partitioning process is done for speeding up of data in the processing process. Partitioned RDD is present in every Spark.

5) RDD Support and operations

Actions

Transformations

6) Explain transformations in Spark

On RDD Transformations are applied functions which lead to another RDD. Until an action happens there is no execution done. For the purpose of transformations map() and filter() are used and where map () applies the functions passed to it on each element of RDD and results in another RDD. The filter makes a new RDD by choosing elements from current RDD that pass function arguments.

7) Explain Actions

For bringing back the data from RDD to the local machine action is the key. For all previously created transformations an action’s execution is the result. Reduce() is an action leading to the functions passed again and again till one value is left. From RDD to local node take() action takes all the values.

8) Define SparkCore functions

Various significant functions like memory management, monitoring jobs, fault-tolerance and job scheduling leads to interaction with the storage data are some of the works done by the Spark Core which serves as the base engine.

Join the DBA course to know more about the basic interview questions that may need to face while attending an interview.

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Memcached And It’s Importance

Memcached distributed caching solution is very much enough for you if you want to develop a high-performance large scale web application. No doubt it is definitely a popular distributed caching system.

In the year 2003, it was created by Brad Fitzpatrick and there are many applications like PHP where they are heavily used.

Working of Memcached :

Sharding of the keys is the basis for Memcached distributed caching architecture. In a dedicated shard, each key is stored that is supported by one or more machines.

For scaling better and caching bulk data is supported by this approach. RAM limit is the maximum set up that a single machine can cache. There are lots of machines added to your system and it will really cache bulk data in the case of Memcached.

For storage and retrieval of the offered key without the knowledge of user about the actual storage is assured by the system.

Popularity Behind Memcache :

There are lots of web applications which is famous in Memcache. Here are few key benefits of using a distributed caching solution called Memcached.

  • Since there is a reduction in IO there is a much faster application and most of the data is served from RAM.

  • Better usage of RAM- There are multiple servers which has lots of RAM left unused and thus you can easily find the machines as nodes to a Memcached system and just use it to the core.

  • Instead of a scale-up application can be scaled out.

Usage of Memcached :

It is a famous library which uses thousands of apps and is very much popular. Here are few popular names that use Memcached.

  • Craiglist

  • Wikipedia

  • WordPress

  • Flickr

  • Apple

Things To Note About Memcached :

It is a very reliable solution but then there are certain things to note about it:

  • RAM storage: Because of the RAM storage of data this makes it much faster and it is very much easy to lose. There is no persistence of data with respect to a storage system. If you find power loss or server crash all the data will be lost in Memcached.

  • As it is frequently found in RAM you need to start the cache after every restart. Thus serving data to cache or storage must be known by the programmer.

  • The persistence and updating of data in various situations must be taken care of by the application developer as there is no persistence in any storage.

  • There are no support transactions done by Memcache and this needs to be a big consideration if you are using a cache transactional data.

  • For producing a lot of garbage in memory it can be CPU intensive.

For more information join the DBA Training Course to make your career in this field.

Stay connected to CRB Tech for more technical optimization and other updates and information.

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Teradata

Define Teradata :

Do you know one of the most famous Relational Database Management System? It is called Teradata. For the purpose of constructing large-scale data warehousing applications, it is mainly suitable. The parallelism concept is achieved by the concept of Teradata. The company called Teradata developed it.

Evolution of Teradata :

Here is a list of achievements and progress of Teradata over the years:

  • 1979: Incorporation was done by Teradata.

  • 1984: First database computer DBC/1012 was released.

  • 1986: Teradata was named by Fortune magazine as Product of the year

  • 1999: Using Teradata with 130 Terabytes was considered as the largest database in the world.

  • 2002: With Partition Primary Index and Compression, Teradata V2R5 was released along with compression.

  • 2006: Master Data Management Solution is done by Launch of Teradata.

  • 2008: Active Data Warehousing was released by Teradata 13.0.

  • 2011: Advanced Analytics Space has been entered by Acquired Teradata Aster.

  • 2012: Version 14.0 of Teradata was introduced.

  • 2014: Version 15.0 of Teradata was introduced.

Features of Teradata:

Here are few features of Teradata:

No Sharing Architecture: Shared Nothing Architecture or No Sharing Architecture is the other name for Teradata architecture. The disks linked with AMPS, Teradata Nodes, and Access Module Processors (AMPs) work independently. There is no sharing done with others.

Parallelism that is unlimited: Massively Parallel Processing (MPP) Architecture is the basis for Teradata database system. The workload is divided evenly across the entire system by MPP architecture. Among its processes, the tasks are split by the Teradata system and run a parallel system for being sure of a completed task quickly.

Linear Scalability: There is a high scalability of Teradata systems and the scaling limit is up to 2058 Nodes. For instance, the capacity of the system can be doubled by doubling the number of AMPs.

Connectivity: Channel Attached systems are connected with Teradata like Mainframe or Network attached systems.

Mature Optimizer: One of the matured optimizers in the market is called Teradata optimizer. Since the starting, it can be designed in parallel. For each release, it has been refined.

SQL: For interacting with the data stored in tables, Teradata supports industry-standard SQL. Apart from that, it offers its own extension.

Robust Utilities: Robust utilities are offered by Teradata for importing or exporting data from/to Teradata systems like FastLoad, FastExport, MultiLoad, and TPT.

Automatic Distribution: The data is distributed automatically with Teradata evenly to the disks and there is no need of any manpower over here.

Join the DBA Course to know more about Teradata effectively.

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Important Things About Hadoop and Apache Spark

In the big data space, they are seen as competitors but the main feeling is that they are better together with growing consensus. If you go through an reads about big data you will get to know about the presence of Apache Spark and Hadoop. Here are their brief overlook and comparison.

1) There are lots of things they do:

The two big data frameworks are Hadoop and Apache Spark but there is no same purpose that is actually served. Across various nodes, it shares massive data collections inside a cluster of commodity servers that you need not buy and handle commodity servers and it means you don’t need to buy or maintain expensive custom hardware. A data processing tool in spark, on the other hand, works on distributed data collections and it doesn’t do shared storage.

2) They both are independent:

There is not only just a storage component in Hadoop called Hadoop Distributed File System as you can also find MapReduce a processing component and there is no need of a spark to get it done. It is possible to use Spark without the need for Hadoop. There is no own file management system in Spark and it needs to be combined with one apart from that if HDFS is of no use then you can find another cloud-based data platform and the Spark was designed for Hadoop, however, there are lots of people who agree that they work better together.

3) Spark is faster:

MapReduce is generally slower than Spark because the latter’s way of processing the data. The operation of MapReduce is done in steps throughout the data in one fell swoop. This is how the MapReduce workflow looks like, “ the cluster reads the data work an operation and the clusters are written with results and the cluster reads the updated data and the next operation is performed, produce next result to the cluster etc. In memory and in near real-time the Spark completes the full data analytics and the data from the cluster is read for working all requisite analytic workings. Thus Spark is 10 times faster than MapReduce and 100 times faster than in-memory analytics.

4) Spark’s speed is not required for you:

If your data operations and reporting requirements are mostly static and you can stay for batch mode processing then your MapReduce processing would be just fine. On streaming data, if you need to do analytics like from sensors on a factory floor or possess applications needing multiple operations, then you need to go with Spark. For instance, there are lots of operations required and common applications for Spark are a real time marketing campaign, along with online product recommendations, analytics, machine log monitoring etc.

Thus join DBA Course to know more about Hadoop and Apache Spark.

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APACHE IGNITE

An in-memory computing platform called as Apache Ignite can be inputed between a user’s application layer and data layer. From the current disk-based storage layer into RAM, enhancing six orders of magnitude and performance.

For handling peta bytes of data to which the in-memory data capacity can be easily scaled. Both the ACID transactions and SQL queries are further supported. Scale, performance, and comprehensive capabilities far above and beyond what traditional in memory databases, data grids are offered by Ignite.

For ripping and replacing their existing databases there is no need of users for Apache Ignite. It works with NoSQL, RDBMS, and Hadoop data stores. Fast analytics, real-time streaming, high performance enabling are some of the Apache Ignite highlights. A massively parallel architecture, used a shared, affordable commodity for current or new applications power. On premises, Apache Ignite can be run and on cloud platforms like Microsoft Azure, and AWS are in a hybrid environment.

Key Features

There is an in-memory data grid for handling distributed in-memory data management and it is contained in Apache Ignite. You will find object based, ACID transactional, failover, in-memory key value store, etc. On the contrary to traditional database management systems, primary storage mechanism are used by the Apache Ignite.

Instead of disk if you are using the memory then it increase its speed upto 1 million times faster than traditional databases.

Free-Form ANSI SQL-99, compliant requires with actually no limitations is supported by Apache Ingite. There are use of any SQL function, grouping, or aggregation, and it aids distributed, non co-located SQL joins and cross cache joins. The field queries concept of backing up to reduce the serialization and network overhead is also supported by Ignite. A computer grid for enabling parallel in memory processing is included in the Apache Ignite. There are other CPU-intensive or other resource-intensive tasks like traditional MPP, HPC, fork-join, and Map Reduce processing. For Standard Java Executor Service asynchronous processing is backed up by Apache.

Join the DBA course to make your career in this field.

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