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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How Cosmos DB Handles Data Consistency

Cosmos DB users with limited percentage will use this approach for data consistency in the real world. Advantages of three alternative consistency models will be used instead. Turing Award Winner Leslie Lamport is based on the work. Thereby creating a database for managing the real life situations were the foundations and they deliver shared applications without the traditional consistency model penalties. Staleness is bounded by the first alternative consistency model that offer a point that there is a sync between reads and writes. There is no guarantee before it but after it the latest version is always accessed by you. Either a number of versions or time interval you will define the boundary.

Everything is consistent outside the boundary and within it there is no surety of a read returning latest data. An element of strong consistency is a store while offering you low latency and the choice of global sharing and higher reliability.

If you want to be sure of all the consistent reads then you can use this model. The writes are also considered to be fast. If you read it in the region that is written then you can get the data that is correct.

Session consistency, second alternative consistency model, works well when there is a read and write drives from a client app. The own writes of a client can be read by them, and across the rest of the network the data replication takes place. You have low latency of data access with this way and you know that you will fail over over a period of time and in any Azure region your application will run.

A third alternative consistency model with prefix of consistency in Cosmos DB has been added by Microsoft. The eventual consistency’ speed has added predictability with Consistency prefix. The latest write might not be seen by you when you read the data but your reads will never be out of order.

It is both fast and predictable useful feature. Your client will be able to see after Write A, then B, and then C, only A or A and B but never A and C.

The Cosmos DB regions will mix on A, B, and C, offering you reliability and speed. From the competition it is a very different beast when Cosmos DB is concerned. There are some NoSQl offers from the limited form of shared access, but they target at redundancy offers and recovery from disasters.

There are similar features offered by the Google spanner but is possible only datacenters in a single region. If your target audience is only US or EU then you might be fine with the work but for a global reach there are more cloud services.

Strong consistency in low latency is a good option but has less value when data replication that is cross-regional becomes a major bottleneck.

CosmosDB relies on applications and that is what your type of consistency choice is all about. Is your target about reading or writing the data? Usage of data? There are advantages and disadvantages of each consistency model and you need to take into account before choice preferences carefully.

A good place to start, session consistency for most app-centric data. It is good to test with various choices when you dont require immediate access of data globally.

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CrateDB In Detail makes CrateDB.For the purpose of receive sensor data similar to IOT you will find CrateDB, a quasi-RDBMS.

Creators of CrateDB may be have a little show for realizing that the “R” part was required but are playing catch-up with regard.

Austrian guys from Berlin discovered an outfit called as and it is being converted into a San Franciso company.

There are 22 employees and 5 paying customers in

There are large number of production users in and it has clearly active custers and overall product downloads.

An open source CrateDB in an essence has less mature solution to MemSQL. The choice for MemSQL and CrateDB exists in part because analytic RDBMS vendors didn’t end it.

There Are No Relational Story Starts In CrateDB:

  • There are original values or objects in a column and these objects are nested/hierarchical structures that are common in the NoSQL/internet-backend world.
  • But when they are BlOBS they are different (Binary Large Objects).
  • For strict schemas manual definition on the structured objects a syntax for navigating the structure in WHERE clauses is required.
  • For automatically inferring dynamic schemas it is simple enough for more suitable development/prototyping than for serious production.

An instance of data given by Crate from greater than 800 kinds of sensors being collected together in a single table. This provides a significant complexity in the FROM clauses. In a relational schema it would be at least as complicated and probably worse.

For knowing the the architectural choices for Crate is to observe that they are accepting to have different latency/consistency standards for:

  • Single row look ups and writes
  • Aggregates and joins

Thus It Makes Sense That:

  • In CrateDB data is banged into an NoSQLish type of way as it arrives, with RYW consistency.
  • The required indexes for SQL functionality are updated in microbatches as soon as possible after that.

There are no real multi-statement transactions that CrateDB will have but it has easier levels of isolation that is called transactions in some marketing contexts.

Highlights of Technical CrateDB Includes:

  • JSON documents are stored from CrateDB records.
  • Relational case purely has the glorified text strings of the documents regarded.
  • IT was found that BLOB Storage was somewhat isolated from the rest.
  • The sharing story of CrateDB initiates with consistent hashing
  • The convenient and the lenient nature have many local shards.
  • There is a possibility to change your shard counts and the future inserts will be given into the new shards set.
  • CrateDB has two indexing strategies with respect to consistency models.
  • Primary-key/ single row look ups have a forward lookup index whatever it is.
  • Columnar index are available in Tables.
  • There are more aggregations and complex queries required and are commonly done straight against the index of the columnar.
  • The indexing strategy and CrateDB’s principal columnar looks like an inverted list which looks like a standard text indexing.
  • Geospatial datatypes can be indexed in different ways.

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How To Get Rid Of Big Data Analytics Failure?

Game-changing initiatives of Big Data analytics are offering you insights for assisting the blow past the contest, provide new revenue sources, serving better customers, etc. Colossal failures are also possible because of big data and analytics initiatives. Thus it leads to money and time waste and not tell the loss of professionals who are talented in technology because management blunders. Considering the fact that you have done the basics which divides success from failure in big data analytics is for dealing with technical issues and challenges for analyzing big data. For staying on the success side of the equation this what you can do.

1) Dont Choose Big Data Analytics Tools Hastily

There are lots of technology failures rise up from the fact that companies buy and use products that stands for an awful fit for their choice of accomplishment. Big data or advanced analytics of the words can be slapped by and seller in their product descriptions for taking advantage of the high level hype around the terms. Around the storage architecture and data transformation there are some basic capabilities for all the big data analytics. Development of a data model is required by every data analytics tool in the back-end system. For translation into business language the right data should always be used.

2) Make Sure That The Tools Are Easy For Use

It is a known fact that Big data and advanced analytics are not simple but the products are very simple and users rely on it for accessing and making sense of the data. Offer simple, effective tools for the teams of business analytics and for using the data discovery analytics and visualizations. For domain registrar GoDaddy the right combination of tools was tough to find. For faster visualizations it needs to be simple but capable for deep-dive analytics. For performing more advanced analytics its team was freed up. Programmer level tools are not provided to nontechnical business users.

3) Project And Data Alignment

The efforts of big data analytics bugging my might fail because they end up as solution while searching the problem that is not in existence. In such cases business challenges/needs must be framed when you are focused into the right analytical problem. There is a need for applying the right data for extracting business intelligence and make proper predictions. Therefore data should have high priorities.

4) Don’t Skip On Bandwidth And Build A Data Lake

There were lots of data involved for big data. In the ancient times, very few companies store so much data, very few organize and analyze it. High-performance storage technologies, large-scale processing are available widely, cloud and on-premises systems are available in the cloud. An important real-time analytics to traffic routing from social media trends needs to be speedy enough. So use the fastest interconnect available for building your data lake.

5) High Security In Every Facet Of Data

The computational infrastructure and its heterogeneity has a higher degree of components and is sped substantially and the ability for meaningful insights from data. Deployment of the basic enterprise tools must be the security measure data encryption whenever identified, practical and assess the management, network security.

6) Data Management And Quality At A Top Priority

Quality and good data management assurance should be the landmark of all the projects of big data analytics or else the chances of failure are much higher. Data management professionals are hired by big part of governance and data quality assurance. After offering strategic importance and initiatives, enterprise have real data ownership need over stewardship of data, management, governance, and policy.

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The ultimate motto of CockroachDB is for making the data easy. Instead of wasting your energy in banging with database shortcomings,and invest that time, money and engineering into transforming a company stronger and better.

Horizontal Scaling

At its lowest level let us see cockroachDB running on a single machine, even though the data is organized logically into rows, tables, columns, etc. At the least level, individual pieces of data are stored on-disk in an arranged key-value map.

The empty range of key-value data starts off and encompasses the entire key space. With more data in this range, it will lead to a threshold size. The data splits into two ranges and single data can be tested as individual units and each covering segment that is contiguous of the key space.


There are three nodes replicated with each range and there can be various configured nodes but the most useful consideration is three and five. In disparate datacenters replicas can be intended to be located for its sustainability.

In multiple machines you can find the stored data and it is very important that the consistency of the data across replicas. Raft is used by cockroachDB and it has a consensus protocol. An independent instance is given for each range of the raft protocol and we many ranges for independent running Raft.

Distributed Transactions

Strong consistency and full support of shared ACID transactions is the basis of CockroachDB and it offers shared transaction using MVCC Multi Version Concurrency Control. Stored data in MVCC is managed on each local storage device with a hint of RocksDB.

Deployment and Management

CockroachDB has been built to have a simple deployment that has proper fit with the container model. You can find the nodes very organized and with symmetry and it has no single point of failure and has no configuration with complications. There is no mention of different roles to be managed. The only thing required is a single binary running on each node in order for joining the cluster and the local stores export for getting new writes and rebalances.

Corporate dev cluster, laptops or private cloud can be used for running the CockroachDB. It is also found on many common infrastructure. You can set up a cluster of Amazon Web Services using cockroach-prod and it can be used in Google Cloud Engine.

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Apache Spark

An in-memory data processing engine which is rapid, elegant, and expressively develops APIS for allowing data workers for efficiently executing stream and machine learning SQL workloads for the need of rapid access of datasets in an iterative manner. Developers omnipresently create applications with the spark running on Apache Hadoop YARN, derive insights, and developing their data science within a single workload, distributed dataset in Hadoop. This architecture offers the basis that permits Spark and other applications sharing a cluster common to the dataset while consistent ensuring levels of service and response.


As the momentum increases in Apache Spark, customers across various sectors can be seen seeking actual values using it. Here are few examples on the usage of Spark:

  1. Insurance: Claiming reimbursement procedures are optimized with the use of Spark’s machine learning skills for processing and analyzing the claims.
  2. Healthcare: Spark core is used for building a patient care system and streaming and SQL.
  3. Retail: For analyzing point of sale use spark data and coupon usage.
  4. Internet: For identifying the spark’s ML capability get to know the fake profiles and develop the product matches that reveals their customers.
  5. Banking: For predicting the retail banking’s profile use a machine learning model for the users financial product.
  6. Government: Spending across time, geography, and category and its analysis.
  7. Scientific Research: Time, depth, geography and future events prediction for analyzing earthquake.
  8. Investment Banking: Stock prices analysis with intra-day for predicting the future price movements.
  9. Geospatial Analysis: Uber trips are researched by geography and time for knowing the future pricing and demands.
  10. Twitter Sentiment Analysis: For finding negative, positive, and neutral sentiments research large volumes of tweets for particular products and organizations.
  11. Airlines: For predicting airline travel delays build a model.
  12. Devices: Choose likelihood of building with extra threshold temperatures.

What’s New in Spark 2.0?

This marks a big milestone for the project and it has new releases in targeted feature enhancement relying on community feedback. With respect to Spark’s enhancement there are four major areas of improvement.


SQL is the famous Apache Spark based applications and the most popular interface. 99 TPC-DS queries has spark2.0 offering support that is largely relied on SQL: 2003 specification. Current data loads can be ported into a spark backend with less replications of the application stack.

  • Machine Learning

A major emphasis in the new release is called machine learning. The package that as new relies on DataFrames and will be replaced with the current Spark Mlib. Models and Pipelines of machine learning can now be persisted across all languages back up by Spark. Generalized Linear Models, K-Means, Survival Regression and Naïve Bayes are now backed in R.

  • Datasets:

For scala and java programming languages the data frames and datasets are now unified within the new Datasets class and it also offers an abstraction for structured streaming. Hive context and SQL context are now overwritten by unified SparkSession. For backward compati bility old APIs have been depricated.

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Important MySQL Performance Tips

There are many MySQL performance issues for turning out with similar solution and making troubleshooting and tuning MySQL a manageable task. You can find 4 tips for getting great performance in MySQl.

1) MySQL performance tip No.1: Workload Profile

If you profile the workload of the servers then you can very well understand how your server spends its time. You can most expose the costly queries for further tuning. You find time as the most important metric as you issue a query against the server, you lack care about anything except how rapidly it completes. With a tool such as MySQL Enterprise Monitor query analyzer is the best tool for profiling your workload. The server executes the tools captured queries and return a table of tasks sorted by reducing order of response time, instantly bubbling up the most expensive and time-consuming tasks for the top as you can see where to focus your efforts. Similar queries together with workload-profiling tools group allowing you to see the queries that are slow along with the queries that are fast but executed many times.

2) MySQL performance tip No. 2: Understand The Four Fundamental Resources

A database server requires four fundamental resources for functioning like CPU, Memory, disk, and network. If you find them erratic, weak, or overloaded then the database server is very likely to perform poorly. It is very significant for understanding the performance of fundamental resources and choosing hardware and troubleshooting problems. Choosing the hardware for MySQL, ensures good-performing components all around. Significantly balance them reasonably well against each other. Companies will select servers with fast CPUs and disks that has memory starvings.

3) MySQL performance tip No. 3: Don’t Use MySQL As A Queue

Queue-like access patterns can get into your software without the knowledge of it. For instance, if you keep the status of an item in such a way that a particular worker process can declare it before acting on it. Then you developing a queue unwittingly. Emails for marketing such as unsent, sending them, then marking them as sent is known example. There are two major reasons for which queues cause problems. Workload is serialized avoiding tasks for parallel work and often the result in a table contains work in process for historical data from jobs that was processed long ago.

4) MySQL performance tip No. 4: Filter Results By Cheapest First

MySQL optimization can be done in a great way by doing cheap and imprecise work first, resulting set of data, precise work on the smaller. For instance, if you are looking for something in a given circle of a point in geographical way. The great circle (Haversine ) formula is the first tool in many programmers for computing distance in the surface of a sphere. The formula requires a lot of trigonometric operations and is very CPU-intensive. There is a slow way of running great-circle calculations and make the CPU machine utilization skyrocket. To small subset of total pare down your records before implementing this great-circle formula. A circle within a square has an easy way of doing it. In this way the only things within the square gets hit by those costly trig functions.

Before applying the great-circle formula, pare down your records to a small subset of the total, and trim the resulting set to a precise circle. A square that contains the circle (precisely or imprecisely) is an easy way to do this. That way, the world outside the square never gets hit with all those costly trig functions.

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Apache KAFKA

Apache kafka is a scalable, fast, durable, and fault-tolerant subscribe messaging system on later stage. Traditional message brokers often uses Kafka like AMQP and JMS as its higher result, reliability and replication. Apache Hbase, Apache storm has a combination of work with Kafka for rendering streaming data and real-time analysis. Geospatial data can be messaged from a fleet of long-haul trucks or data sensor from cooling and heating equipment in office buildings. Whatever may be the scenario, for low-latency analysis for massive message streams in Enterprise Apache Hadoop.

What KAFKA Does ?

There are a wide range of use cases and a general-purpose messaging system is supported by Apache Kafka and it has a high throughput, reliable delivery, horizontal scalability is significant. Apache Hbase and Apache Storm has a good work compatibility with Kafka. These are common use cases:

  • Website Activity Tracking
  • Stream Processing
  • Log Aggregation
  • Metrics Collection and Monitoring

Some of the Significant Characteristics of Kafka making have an attractive option for use cases with the following:

1) Scalability: With no downtime distributed system scales easily.

2) Durability: Provides intra-cluster replication and persists messages on disk.

3) Reliability: Supports multiple subscribers, Replicates data, and balances automatic consumers in case of failure.

4) Performance: For publishing and subscribing you require high throughput along with disk structures for offering constant performance even with many terabytes of stored messages.


It can be considered as a shared commit log, and incoming data has a sequential entry into disk. Here are four main components involved in moving data in and out of Kafka:

  • Producers
  • Topics
  • Consumers
  • Brokers

A Topic is a user defined category in Kafka where messages are published and it is done by Kafka Producers for one or more topics and Consumers give their subscription to process and topics for the published messages. A cluster of Kafka comprises of more servers known as Brokers for handling the replication and persistence of data message.

Kafka’s performance would be high and it is the simplicity of the broker’s responsibility. The topics of Kafka comprises of more partitions that are ordered with immutable message sequences. Sequential writes are available in partition because the design greatly reduces the number of hard disk seeks.

Kafka’s performance contributing factor and scalability is the fact that brokers of Kafka keep track of the messages that has been consumed and the consumer must take the responsibility. JMS, traditional messaging system have the broker bore the responsibility, there by strictly restricting the ability to scale as there is a steady increase in consumers.

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CouchDB VS Couchbase

For employing NoSQL database is a trend in the modern application system development for cloud or local platforms. It is understood that NoSQL stands for “Not the SQL relational database” but precisely it is “Not only the Sql relational database”. It can manage structured datasets well in a traditional way but doesnot accept to the RDBMS traditionally by backing up semi-structured, unorganized data. Let us see what are the advantages of NoSQL and compare and contrast its features between Couchbase and CouchDB.


Storing network or graph types are done using Graph stores for social networking, like Neo4j and Giraph.

Cassandra and Hbase are the column oriented storage and data of different columns are stored together rather than rows.

A document called as a key plus complex data structure is called as document database for instance MongoDB, Couchbase, and CouchDB.

The simplest NoSQL called as the Key-value stores, where each item is stored as a key + value.

In a hybrid mode there are some NoSQL databases built. For instance, Couchbase has both a key-value store and a key-document store is available in Couchbase.

CouchDB vs Couchbase

Earlier there was an affinity towards the similar names. Behind each of these names there is a story. Damien Katz initiated the Couchbase, who was actually the founder of CouchDB, a combination of CouchDB and Membase is called Couchbase, leading to make an easily scalable and high performance database.

1. Open Source Type

An apache open source project written in the Erlang language is CouchDB and is freely downloadable by the user. Another open source language is couch base, but it has community, enterprise and developer editions as a group of components.

2. Database Lock

For ensuring the table or a row, DBMS will use lock and that is what traditional DBMS is all about. In the CouchDB there is no lock as it uses a concept called MVCC (Multi Version Concurrency Control). Pessimistic locking is used by Couchbase on the other hand.

3.Query language

Couchbase has its own query language called N1QL, a SQL-like query language for JSON. CouchDB doesn’t have Query language. They both have similar views that is multi-dimensional/geospatial.

4. Topology

Couchbase topology is distributed and it means it is built from forms and scratch a cluster of nodes. The owner for a portion of hash space has each node in the cluster. On the other hand Couch DB is imitated and is master-master replication, making multi-site application easy for deployment. In the form of key-document MongoDB is widely used in application development. It has pros and cons over CouchDB and Couch database.

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Difference between NoSQL and MySQL

For every couple of years you will find the world’s data doubling as per the experts. There has been a highlight in the epic increase of big data in the recent times and there are restrictions of reliance and traditional forms of data storage and management and focused attention on new methods for seeking the volume and variety of organized and unorganized data.

Maintained in racks of folders the data was stored in olden days in physical files and it was filling the entire rooms in the large corporation offices. The flat file database was changed after the inventions of computer and go-to technique.

SQL has been in demand since 1970s with respect to IT infrastructure of companies. Today RDBMS based SQL implementation of the Internet, now fuels very high scale sites like Facebook, Google, Twitter, and even Youtube. The world’s most popular database is MySQL and remains so because of its open source nature.

The new buzzword in the rapid changing technology database world is NoSQL. With a projected growth forecast for reaching 3.4 $ billion in 2020 the indusry is a formidable one and it gives a compound annual growth rate (CAGR) of 21% for the period 2015-2020.

You might be wondering what is NoSQL? It is a database technology different from MySQL and it is basically because it doesn’t provide the Structured Query Language.

Sql vs. NoSQl is the comparison of relational vs non-relational databses . So let us the key differences between MySQL and No SQL databases?


Here are few highlights of some of the biggest advantages and disadvantages of NoSQL.


Table-less as it is non-relational: They are different from SQL database as they are non-relational. It means they can be handled easily for managing and they offer flexibility at a higher level with newer data models.

Low-cost and open source: An appealing solution for smaller companies with restricted budget is the nature of open source NoSQL databases.

Map reduce and easier scalability through support: Experts of the No SQL database often provide elastic scalability for major selling point for NoSQL. It is designed for backing up on full throttle and even with low-cost hardware.

A detailed database model is not required: The nature of a non-relational NoSQL database permits architects to quickly create a database without offering to develop a brief database model. A lot of development time is saved over here.


Not well defined community: It continues to grow rapidly for the NoSQL group as it is relatively fresh and does not have the MySQL user base maturity. Although it is growing it is difficult to beat the vast network of highly experienced end users.

Reduction in reporting tools: NoSQL databases has a very big problem as it doesnt have reporting tool for testing the performance and analysis. But here in MySQl you can get a wide array of reporting tools as a proof for your application validity.

There is no standardization: It requires a standard query language like SQL for the No SQL to grow. Microsoft researchers highlighted the issue for claiming the NoSQL standardization lag which can lead to problem migration. For the database industry other than this standardization is required.

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