Monthly Archives: July 2017

Difference between MongoDB and Couchbase

A significant database server MongoDB with its great specification for elective storage engine called Wired Tiger. It has a provision of writing capability for the MongoDB server about 10 times more than the normal one.

For data standardization the data need not be kept in the memory. The number of read and write operations performed with both the servers is the same. Outside the memory, the data is used and it well explain us the ways these two servers perform.

The read and write work can stay in the mode which is inactive and waits for a maximum of 5 milliseconds. MongoDB and Couchbase performance server has the determination in the level they both perform as the number of users keep increasing till the 5 milliseconds are overcome by the read and write inactivity. They operate differently from each other.

Data Model

Both documents and key value can be seen din Couch base in terms of data models, but the data model is only document type in MongoDB. Every document will start with a key value as the documents have their keys. Both query and index services can be used for the query.

Query

N1QL has Couch base server along with Ad-hoc views and key-values. Ad-hoc can be seen in MongoDB query, and MapReduce aggregation.

Concurrency

The couch base server in terms of concurrency has both pessimistic as well as optimistic locking whereas MongoDB server also has the same but with an optional store machine known as WiredTiger. The quality of work rapidly humiliates MongoDB’s with increasing number of customers. It cannot entertain various customers but the instance the increasing number of customers, MongoDB starts reversely.

Storage

The capacity of holding the binary values about 20 MB whereas MongoDB server has the ultimate capacity for storing huge files into a number of documents. The server can have larger binary values and still continue to use Couchbase server along with isolated storage service for bearing the metadata on the binaries.

Scaling

Master-master scaling model is distributed as a Couchbasewhile the MongoDB has both master and slave duplicate sets as its scaling model. From a particular duplicate set it is very tough for MongoDB to set an entirely fragmented frame. It is a big complicated process with huge variety of movable parts along with physical structure. There is no master in Couchbase and it holds a duplicacy of its original document during the data failure the duplicate file can be utilized.

Fragmentation

The data is fragmented by the Couchbase and then counts horizontally by spreading hash space for all the nodes in the cluster of data. The key present in the each document decides the particular node of hash space. MongoDB usage and fragmentation of data can be done by selecting a key in the entirely documented base.MongoDB depends on Couchbase for choosing the fragment key and while the couchbase server does the fragmentation on its own without any human effort.

Facility Of A Mobile Resolution

You need to include your own code for the apps as the MongoDB does not support mobile applications. You need to be sure about the internet connection and the Couchbase supports entirely by developing apps that can include with or without the internet.

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What is Data Integration?

Combination of data from several different sources is called data integration that is sored using lots of technologies and give a unified view of th data. There is an increasing significance in cases of merging systems of two companies or integrating applications within an organization for providing unique view of the company’s data assets. Data warehouse is the later initiative. The most famous application of data integration is building an enterprise’s Data Warehouse. In the source system , this would not be feasible on the data available.

Data Integration Areas

  • It covers several distinct sub-areas like:
  • Data Warehousing
  • Data Migration
  • Enterprise application/ information integration
  • Master data management

Difficulties in Data Integration

The biggest difficulty is the technical implementation of integrating data from different and incompatible sources. A much difficult challenge is the data integration. These are the following phases:

Design

  • The data integration should be an initiative of a business and not IT. There is a need of a professional for understanding the assets of the data for leading the discussion about the long-term data integration initiative for making int consistent, beneficial, successful.
  • You should analyze the requirements like the reason behind the data integration,objective and deliverables, data sources, availability of data for fulfilling the requirements, business rules, support model and SLA.
  • Analyze the source systems that is the options of extracting the data from the systems, required/available frequency of the extracts, quality of the data, required data fields populated frequently and properly, documentation available, system owner.
  • Data processing window, system response time, estimated number of users, data security policy, back up policy are some of the non-functional requirements.
  • Support model for the new system and SLA requirements
  • Owner of the system, upgrade expenses, maintenance.
  • Document the above result in STS and confirm it from all parties participating in this project.

Implementation

A feasibility study is performed based on the SRS and BRS for selecting the tools and implementing the data integration system. There are some small companies and enterprises that start with warehousing the data and faces the decision making about the set of tools they require for implementing the solution. The enterprise which all already initiated the projects of data integration in easier position they already experience the extended existing system and exploit the knowledge present for implementing the system more effectively. You can find cases for utilizing a new, apt platform with good suits or technology effective with respect to staying with current company standards.

Testing

A proper testing strategy is required along with the implementation for ensuring the correctness of the unified data, up-to-date, and complete. Both organizational requirements and technical IT participate in the testing for ensuring that the results are expected/required. For incorporating the testing Performance Stress test, Technical Acceptance Testing, and User Acceptance Testing is required at the least.

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Apache SOLR

An open source platform for searching data stored in HDFS in Hadoop is called Apache Solr. The search and navigation features of Solr powers have many of the world’s biggest internet sites, providing full power text search and real-time indexing. Geo-location, text, tabular or sensor data in Hadoop find it quickly with Apache Solr.

What does SOLR do?

In Apache Solr Hadoop operators put the documents by indexing them via XML, JSON, CSV or binary over HTTP.

HTTP GET seeks petabytes of data by users querying them. JSON, XML, CSV or binary results can be perceived by them. They are optimized for high volume web traffic.

Best features include:

  • Standard-based open interfaces lik JSON, XML and HTTP

  • Advanced full-text search

  • Comprehensive HTML administration interfaces

  • Near real-time indexing

  • Linearly scalable, auto index replication, auto failover and recovery

  • Flexible and adaptable, with XML configuration

  • Server statistics exposed over JMX for monitoring

Highly tolerant, reliable, scalble are some of the properties of Solr. The data analysts, developers in the open source community trust shares indexing of SOL’S imitation and load-balanced capabilities for querying.

Working of SOLR:

A Java written SOLR runs as a standalone full-text search server inside a servlet container like Jetty. Apache Lucene Solr uses Java seach library at thec ore for full-text indexing and search with REST-like XML.HTTP and JSON APIs making it easy for use with many programming languages.

A strong configuration of SOLR permits it to shape almost any type of application without Java coding, and it has a plugin architecture which is extensive more advanced customized and is required.

A deployment methodology of setting up cluster of SOLR servers combines fault tolerance and high availability. Distributed indexing is provided by SOLR CLOUD for offering automated fail over for queries in the event of any failure to a SOLR CLOUD server.

  • INDEXING AND SEARCHING TEXT WITHIN IMAGES WITH APACHE SOLR

Most of the users provide common request for enabling the index text in image files; for instance, text in scanned PNG files. How to do it with SOLR is what this tutorial is all about. There are some downloads of prerequisites of hortonworks Sandbox finish studying the ropes of the HDP Sandbox tutorial, Step-by-step guide.

  • Searching and Indexing documents with Apache Solr

We will see to how to run SOLR in Hadoop with the index in this tutorial stored on HDFS and using a map reduce jobs for indexing files.

  • Customer Sentiment and social media is analyzed with Apache NiFI and HDP search

You can dig Twitter, Facebook and other social media talks for analyzing the customer sentiment about the person and competition. You can be more focused using the Big data, decisions, real-time, etc.

For more information watch the video of how to refine raw data in Twitter using HDP.

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Apache HBase

On top of HDFS, a non-relational database called Apache Hbase keeps running. An open source NoSQL database offering real-time read/write access with large datasets is nothing but the Apache Hbase.

For linear handling of huge data sets with lots of rows and columns Hbase is the best and it merges effortlessly the data sources that use a wide variety of different schemas and structures.

Combined with Hadoop it works smoothly with the data access engines through YARN.

Working action of Hbase:

Random, real time access is provided to your data in Hadoop is provided in random by Apache Hbase. For very hosting very large tables and providing a great choice for storing multi-structured or sparse data. Hbase can be queried by users at a particular point of time, making possible flashback queries.

Every table requires to have a primary key as Hbase scales linearly. Allocated to a region are few sequential blocks which is again a sub division of divided key space. Owning one or more regions of Regionservers for loading uniformly across the cluster. With frequent access of keys within a region, Hbase is further splitted automatically as a manual data split is not necessary.

Hmaster and Zookeeper servers record information about topological clusters available with clients for connecting them and downloading a list of Regionservers for key ranges hosting the regions. Without any need for central co-ordinator Client can directly take up the data from the database. A memstore is included for cache immediately in the memories.

Below are the following characteristics with a great choice for offering semi-structured data like log data thereby offering data very quickly to users or applications integrated with Hbase.

Characteristic :

  • Fault Tolerant

  • Fast

  • Usable

Advantages :

  • Imitating across the data center

  • Highly consistent and atomic row-level applications

  • Automatic fail over and higher availability

  • Partitioning of large databases and load balancing of tables

  • Real time lookups

  • Block cache and bloom filters for In-memory caching

  • Filters and Co-processors for Server side processing

  • Accomodating wide range of use cases for the data model

  • File and Ganglia plugins for metrics exports

  • Thrift and REST gateway with easy Java API

Apache Hbase usage enterprise with low latency storage for scenarios requiring analysis of real-time and data in tabular format with end user applications. Web security services offering company maintaining a program for holding lots of event traces and activity logs from it customer’s desktop on a daily basis. For tightly integrating security solutions along with Hbase for integrating company’s programmers. (For assuring the security they offer have pace with real-time changes in the threat landscape).

A stock market ticker plant data is provided by another company queried by its users more than thirty thousand times per second, with only few miliseconds of SLA. Offering Super low-latency access Apache Hbase access an enormous fast changing data store.

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BEST SQL-on-Hadoop Tools That You Need To Know

There are various tools of SQL-on-Hadoop developed that permitted the programmers for utilizing the existing SQL experits on Hadoop data stores. Familiar and comfortable SQL is their motto based on the front end to ask large data stored under Hadoop architecture. Here in this article you will find the best tools to use and check out their advantages and disadvantages

SQL-on-Hadoop Tool: Cloudera Impala

A luxurious provision for the developers for running a user friendly SQL query on Hadoop Distributed File System (HDFS) and Hbase. Hive also provides an SQL like interface, for following the batch processing that lead to lags if something is looking for performance oriented alternative. This lag has been overcome for running queries in real time that allows integration of SOL BI tools with Hadoop data store.

An open source tool like Impala backs up the popular formats like LZO,Avro, RCFile, sequenceFile etc. A cloud based architecture through Amazon’s Elastic MapReduce. The ANSI SQL compatability of Impal says there is a small amount of business disruption as developers and analysts can be productive from the first day without the requirement of any new language.

SQL-on-Hadoop Tool: Presto

There is another help from Facebook that is provided as an open source tool. It has many similarities with Impala and is written in Java:

  • Interactive experience is provided.

  • Considerable groundwork is required that is installation across a number of nodes.

  • The data should be stored in a particular format (RC FILE)for optimal performance.

On the other hand, Presto gives interoperability with Hive meta-store. Combining data from multiple sources is done by Presto and this is a major advantage for enterprise wide deployments. The major difference from Impala is that Presto is not backed up by any of the major suppliers.

Therefore if you plan for getting an enterprise wide deployment you would need to consider other options even though some of the famous technology giants such as Airbnb and Dropbox are ready to use it.

Pivotal :

This is an SQL-on-Hadoop product at the enterprise level capable of handling most of the demands of modern day analytics that tricks most of the boxes. The integrated analytics engine comes with learning capabilities of machine

that enchances the performance with usage. Data analysis, demand with focus on the modern day organizations for query language for handling statistical, mathematical and machine learning algorithms like regression, hypothesis testing, etc.

There are various options at your disposal and SQL experts would gain a lot from the tools for hitting the ground running after choosing the right tool with lots of options at your risk. Do some technical research on the background if you are planning to start in any Hadoop training in the near future.

SQL-on-Hadoop Tool: Shark

With respect to one of the first top SQL-on-Hadoop projects, initiation of Shark as an aliter to have run Hive on MapReduce. The aim is to retain the functionalities of Hive, for delivering superior performance. It has a very good popularity and a faster alternative to Map-Reduce and there are lots of users around the world for it.

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What Do You Mean By PostgreSQL?

An object-relational Database Management and a general purpose system otherwise called as PostgreSQL is the latest open source database system. Postgres 4.2 at Berkeley Computer Science Department, University of California was the basis of the development of PostgreSQL.

For running on UNIX-like platforms the program designed was called as PostgreSQL. It has a good portability with varied platforms like Mac OS X, Windows, and Solaris.

It is a free and open source software. A free open source license called as Postgre license has its source code. PostgreSQL can be easily modified, used, and distributed into any form.

Very less efforts are required due to the stability of PostgreSQL. Applications based on PostgreSQL’s development the cost of the ownership is very less in comparison with other database management systems.

Highlights of PostgreSQL Features

There are many advanced features of PostgreSQL that has to be offered by other database management systems, like:

Sophisticated locking mechanism

Multi-version concurrency control (MVCC)

User-defined types

Asynchronous replication

Foreign key referential integrity

Rules, subquery, views

Nested transactions (savepoints)

Asynchronous replication

PostgreSQL and Their Recent Versions Support The Following Features

Native Microsoft Windows Server Version

Tablespaces

Point-in-time recovery

In each new release more new features are added

How is PostgreSQL Different?

Implementation of multi-version concurrency control of PostgreSQL is the first database management system even before Oracle. A snapshot isolation in Oracle has the MVCC feature. An object-relational database management and a general-purpose system, PostgreSQL helps you to add custom functions developed using programming languages like C/C++, Java, etc.

It is extensively designed for you to declare your datatypes, functional languages, index types, etc. Any part of the system can develop a custom plugin to develop for meeting your requirements in case you dont like any part of the system. Eg: adding a new optimizer.

In case you need support an active community is available for help. PostgreSQL community is available for you to support. All the answers are available in the community to resolve your issues. If you need any support there are companies to offer help.

PostgreSQL Usage

People who use PostgreSQL

There are some products and solutions built by some companies with the help of PostgreSQL. Apple, Fujitsu, Red hat, Juniper Network, Cisco, etc. are some of the featured companies. There are some of the sections that complete the list of organizations for using PostgreSQL just check it out with the PostgreSQL featured users.

There are some fundamental topics in PostgreSQL like

PostgreSQL Select

PostgreSQL Order By

PostgreSQL LIMIT

PostgreSQL IN

PostgreSQL INNER JOIN

PostgreSQL Cross Join

PostgreSQL Natural Join

PostgreSQL Having

PostgreSQL Union

PostgreSQL Insert

PostgreSQL Subquery

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10 Rules For Increasing The Speed of SQL Queries

SQL Developers and DBAs help in getting the faster queries to many time tested methods for achieving the goal. Here are 10 rules for making the database faster and more efficient.

1) Do not use cursors always

There is a drastic decrease of speed if your cursors are used. There could be block of operations between the two which is actually longer than required. Thus concurrency in your system is decreased.

2) If you cannot avoid using them

If at times you are unable to stop the usage of the cursor then run cursor operations against a temp table instead of a live table. The only update statement against the live table is very meager and it is a short time lock holder that can enhance concurrency greatly.

3) Intelligently use the temp tables

There are lots of situations where you can use the temp table for instance if you are joining a table to a large table then there is a condition on that large table for enhancing the performance by taking out the subset of data you require from the large table into a temp table and joining with that instead. For greatly decreasing the processing power this can help you a lot if you have lots of queries in the procedure for making similar joins to the same table.

4) Pre-stage your data

An age old technique that has been overlooked is this technique. You can pre-stage the data by joining the tables if you have procedures that will do same joins to large tables. This will give the results ahead of time and persisting them in a table. For avoiding the large join the reports can be running against the pre-staged table. In most of the environments there are popular tables getting joined all the times but this techniques is not used widely. For saving server resources it is an excellent way and there is a basic point why they cant be restaged.

5) Reduce the use of nested views

For concealing large queries from users views are used but while nesting one view within another that’s again inside another view (so on) you will lead to lack of performance. Your optimizer will return nothing or will slow down if too many views are nested in massive amounts of data returned for every query. The query response times will fall from minutes to seconds if you are reducing the nested views.

6) Prefer table-valued functions instead of scalar

While using a scalar function in the select list of a query for boosting the performance by changing it to a table-valued function and include cross apply in the query. You can reduce the query in half times.

7) Partitions in SQL server

Users of SQL Server Enterprise can take benefit of the data engine partitioning features which are automatic to speed performance. You can find simple tables are created as single partitions in SQL server that can later split into multiple ones as required. For moving large amounts of data between data tables you need to use switch command instead of insert and delete.

8) Delete and update in batches

Large amounts of data from huge tables would be a tough one if you want to delete or update it. Both of them run as a single transaction for killing them if something happens during the work. The system needs to roll back the entire transaction. So in such cases other transactions are also blocked leading to a grid lock state.

9) Take your time

It is not necessary that both deletion and updation should be carried out on the same day. You can take your time by doing the tasks in small batches there by reducing the load on the system.

10) Avoid the ORMs

Majority of issues today are caused by Object Relational Mappers. It is very difficult to avoid them but you can minimize them by writing your own stored procedures and have the ORM to be called.

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What is Data Modeling?

Data Modeling is a process of structuring and organizing data and in a Database Management System these data structures are implemented. They also pose some limitations or constraints within the structure other than organizing and defining the data.

Managing bulk of organized and unstructured Data are a primary function of information systems. Structured data for storage in relational databases, another database management system. There is no description of word data like data processing, pictures, digital audio, video, etc. Logical data model can contain such designs.

Data Model:

It is actually given into 2 parts the description of data structures and the way it is organized. For instance: database management system.

Data Structure:

Describing the data structure with a given domain by implication, structure of the domain itself together comprises of the data model. A dedicated grammar is specified for a dedicated artificial language with respect to that domain by the data model. Each data model is different and the data structured with respect to fine one model is very tough to integrate when compared to another data with respect to another data model. Holding information about the companies, relationship with the entities, and attributes and that is how a data model represents classes of entities. Irrespective of the data representation in a computer system, the model describes the organization of data.

Generic Data Modeling:

Generic data modeling has the following characteristics:

A class, relationships, individual thing are some of the generic entities the data model consist of.

Individual thing or one of its subtypes is an example of every generic entity

Using an explicit relationship classifies every individual thing classified by a kind of thing class.

Classification of classes are defined in a different way as standard instances, such as class of relationship. Reference data are called standard classes. Also standard types of connection, such as ‘is composed of’ and ‘is involved in’ can be described as standard instance.

Set of Rules Followed By Generic Data Model:

1. Representing relationships to other entity types are assigned to candidate attributes.

2. Representation of entity types are named after underlying nature not the role it plays in a particular context.

3. Within a database or an exchange file do have entities of local identifier.

4. Entity types representations are event effective, activities along with relationships.

5. A sub type hierarchy or entity types are defined in universal context as a model. The arrangement of sub types of models are under the entity relationship.

6.A composition relationship defines a relation between two individual things. In reference data, additional constraints are defined.

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8 Features of DynamoDB Success

AWS has launched DynamoDB for the entire world and it is an amazing piece of technology. Here are 8 features to get success by using DynamoDB:

1. Why do you really need DynamoDB?

If the right tool for the job is DynamoDB and you should be aware of it. If you require aggregations or possess a small amount of data or grained ability to combine lots of data together then DynamoDB is not the right choice. In such cases RDS or Aurora is the apt choice and where durability doesn’t matter Redis or ElastiCache is the right choice.

2. Know everything in detail about DynamoDB.

Although everybody reads the document there are few points that are missed like how to use the tool and laying out your data at scale. It is a pretty dense section. There are only few words about stress-testing as DynamoDB is not an open source.

3. For help ask Amazon

For checking the parts of the account AWS has lots of tools so do not worry. Everything from limit increases to detailed technical support, Amazon is always there for help. They are always helpful in getting us in touch with the right people and fast-tracking our support requirements.

4. Please read before you write

The write throughput is five times costlier when compared to the read throughput. If there are lot workloads towards writing then please check whether you can avoid updating it in place. Reading will help you to reduce your cost before writing as it will avoid lots of mistakes especially in a write-heavy environment.

5. Batch Partitioning and writing upstream

If the machine upstream in dynamo receives the key information then you can combine or group the data together and save writing on it. You can just write once per second or minute instead of writing every time you can group together all the information instead. You can manage your latency requirements with batching. Locking or race conditions can be avoided by Partitioning.

6. Throughput on spike and dynamic modification

By auto-scaling your DynamoDB you can get significant savings by a bursty traffic. By releasing the AWS feature you can learn more from the AWS blog. For extra cost savings, you can manage how DynamoDB throughput is offered vs how much is it in use with AWS Lambda and Cloud Watch events.

7. Make use of DynamoDB Streams

A not well-known feature DynamoDB can post all the changes to what is importantly a Kenesis requirement. For developing pipelines, streams are very useful and therefore you are not constantly Log all of your hot shards running SCANS or doing your own program.

8. Log all of your hot shards

While facing throttling error one must log particular key for update. Depending on how your data is laid out DynamoDB will perform differently. AWS engineers run DynamoDB as a cloud service. IT is definitely a great piece of technology. By using it correctly will help you earn more profit.

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What is inside Microsoft’s Cosmos DB?

The CosmosDB launched a new Azure cloud database as a service replacing Document DB which was an earlier choice of the company. Rimma Nehme is the product’s architect demonstrates CosmosDB in build day 1 keynote. She has done her PhD in computers and worked for Mircosoft Research previously. She also was a part of the SQL team.

What’s the point?

Nehme declared that all the developers should use CosmosDB for using Azure based application.

Being a former employee of SQL Server team she gave such a bold statement.

Here are few advocating points for that:

  • It’s fight examined to the max, providing as the database back end-for some of Microsoft’s greatest online services

  • It’s enhanced for low-latency database reads and writes. With the help of Solid State Disk storage and with “latchless” and “lockless” structures of data that surprisingly is similar to SQL Server’s in Memory and it makes the use of that.

  • It facilitates all four NoSQL designs (key-value, documents, column family and graph).

  • Geo-distribution plumbing is not to be worried as the data is retrieved automatically as they come online for Microsoft’s own foundational services.

  • It is not like other NOSQL Database, that pressurize you into a model of so-called “eventual consistency” with respect to to geo-distrubuted propagation of database up-dates, Cosmos DB allows you to select between that design, a relational database-like type of powerful reliability, or three choices in between the two extreme conditions.

  • It’s geo-distributed, across Pink regions/data facilities. It’s already available in all of them, and will be instantly available in new areas as they come on the internet, because it’s a fundamental assistance for Microsoft’s own properties.

  • Despite the NoSQL classification, Cosmos DB does assist its own language of SQL, as well as other APIs (its own JavaScript API and the MongoDB API, with Apache Gremlin and the Azure TableStorage space API available in review, and others on the street map).

  • Unlike many NoSQL data source — or relational data source, for that issue — which can be measly with listing, Cosmos DB indices every line automatically. Developers are free to “opt out” of listing certain content, but they don’t have to “opt-in” to get them.

Do not be careless with your business

May be the top reason for Ms. Nehme’s enthusiasm for CosmosDB is on the business side (she’s got an MBA addition to her PhD): Microsoft is supporting the new database’s efficiency statements with codified service level agreements (SLAs) on its latency, throughput, reliability and high-availability.

As Nehme said, Cosmos DB isn’t just a technology, but a service. Right from the starting it was designed in such a way rather than just a cloud migrated vision of an on-premises product. Business features are core of the platform and services are business.

Dynamic Competition

With the new latest designs of COSMOS DB. It is said that CosmosDB has a tough competition when compared to DynamoDB of Amazon Web Services. The latter has no SLAs in AWS foundational NoSQL database and core to company’s data gravity service.

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