#IBM Netezza training

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[proerptraining]

IBM Netezza Online Training: The Ultimate Guide for IT Professionals

Are you an IT professional looking to enhance your database management skills? Look no further! This comprehensive guide will take you on a deep dive into the world of IBM Netezza, empowering you with the knowledge and expertise to excel in your career. Whether you're a seasoned database administrator or just starting out, IBM Netezza online training offers a wealth of opportunities to expand your skill set and stay ahead in the ever-evolving IT industry.

Introduction:

Let's begin with a brief overview of IBM Netezza and its significance in the IT landscape. IBM Netezza is a powerful data warehousing and analytics platform designed for high-performance database management. With its unique architecture and advanced features, it's the preferred choice for organizations dealing with large volumes of data. Gain a solid understanding of Netezza to open doors to exciting career prospects and efficiently manage your data.

Chapter 1: Understanding IBM Netezza:

Delve into the foundations of IBM Netezza, exploring its architecture, key features, and benefits. Netezza's massively parallel processing (MPP) architecture allows for lightning-fast data processing and analysis, enabling organizations to derive valuable insights in near real-time. Discover how Netezza's unique design sets it apart from traditional database management systems and revolutionizes data management practices.

Chapter 2: Getting Started with Netezza Online Training:

Embark on your learning journey by finding reputable online training platforms that offer comprehensive Netezza courses. These flexible and convenient training programs allow you to learn at your own pace while honing your Netezza skills alongside your professional commitments.

Chapter 3: Key Concepts and Techniques in Netezza:

Dive deeper into the key concepts and techniques that form the backbone of Netezza. Gain insights into Netezza data warehousing, understanding how it efficiently organizes and manages vast amounts of data. Explore SQL queries and optimization in Netezza, equipping you with the knowledge to write efficient queries and extract valuable information from your databases effortlessly.

Chapter 4: Hands-on Exercises and Practice:

Theory is important, but practice is where true mastery is achieved. Follow step-by-step walkthroughs of hands-on exercises to apply your newfound Netezza knowledge in practical scenarios. Gain practical tips and best practices to navigate common challenges and build your confidence in working with Netezza.

Chapter 5: Advanced Topics in Netezza:

Take your Netezza skills to the next level by exploring advanced topics. Delve into performance tuning techniques to optimize Netezza's performance and make the most of its capabilities. Learn about Netezza administration and troubleshooting, equipping you with the knowledge to effectively maintain and support Netezza environments.

Conclusion:

Unlock your full potential as an IT professional with IBM Netezza online training. Mastering Netezza makes you a valuable asset to organizations seeking to harness the power of data. Continue your learning journey, explore certification opportunities, and stay updated with the latest advancements in Netezza. Embrace the limitless possibilities that Netezza offers and propel your career to new heights.

Start your IBM Netezza training today to unlock a world of opportunities!

Contact us for details on IBM Netezza training.

Mail: Rahul@proexcellency.com

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Glory IT Technologies offering IBM Netezza Online Training by certified working experts.

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IBM Netezza Training

High-performance data warehouse appliance and advanced analytics applications by using IBM Netezza

Introduction:

Netezza was obtained by IBM in 2010, September 10 and reached out of support in June 2019. This technology was reintroduced in June 2020 as the part of the IBM. This system mainly built for Data Warehousing and it is simple to administer and to maintain.

IBM Netezza performance server for IBM Cloud Pak with enhancement to in-database analytics capabilities. This technology designed specifically for running complex data warehousing workloads. It is a type of device commonly referred to as a data warehouse device and concept of a device is realized by merging the database and storage into an easy to deploy and manage system.

By using IBM Netezza reduces bottle necks with commodity field-Programmable Gate Array (FPGA). This is a short high-level overview architecture.

Benefits and Features of IBM Netezza:

Achieving frictionless migration

Pure Data System for Analytics

Elimination of data silos

Acceleration of time value

Choosing your environment

Helping reducing costs

Minimal Ongoing administration

Flexible deploys in the environment

Benefits from in-database analytics and hardware-acceleration

Flexible Information Architecture

Solving more business problems while saving costs

Make all your data available for analysis and AI

Review (or) Overview

User login control

Impersonating

Key management

Advanced query history

Multi-level Security

Row-secure tables

CLI Commands and Netezza SQL

 Enable and Disable security commands

   Career with IBM Netezza:

    This IBM Netezza is efficient and reliable platform for enterprise data storage and it is easy to use. This technology is a best solution for larger database. Comparing with other technologies this technology has best career because more than 10000 IBM Netezza jobs available across India.

If you want to learn more about IBM Netezza, go through this IBM Netezza tutorial pdf by Nisa Trainings and also you can learn this IBM Netezza online course yourself by referring to Nisa Trainings on your flexible timings.

Currently Using companies are:

USAA

United Health Group

Quest Diagnostics

Citi

Harbor Freight

Bank of America

IBM

These are the companies using this technology and it is an on-demand technology.

Course Information

IBM Netezza Online Course

Course Duration: 25 Hours

Timings: On Your Flexible Timings

Training Method: Instructor Led Online

For More information about IBM Netezza Online Course, feel free to reach us

     Name: Albert

     Email: albert@nisatrainings.com

     Ph No: +91-9398381825

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IBM Netezza Training

Nisa IBM Netezza Training is an integrated framework with essential design principles of simplicity, scalability, speed and analytical strength. 

IBM Netezza is one of the new advanced technologies that merge database analytics and data warehousing into a high–performance, massively advanced, scalable, parallel analytical platform. It also automates and improves data processing efficiency and handles sophisticated algorithms in minutes.

IBM Netezza appliances are redundant and fault-tolerant systems. IBM Netezza replication services for disaster recovery improves fault tolerance by extending redundancy across local and wide area networks. It protects against data loss by synchronizing data on the primary system (the master node) with data on one or more target nodes (subordinates). These nodes make up a replication set. It is one of the new advanced technologies that merge in-database analytics and data warehousing. 

After this IBM Netezza training, you should be able to recognize how the Netezza architecture and parallel processing capabilities help modelling and analysis paradigms in large-scale data sets. In this context, you should fully understand data mining approaches to solve common business problems in real time.

 Course Content:

·         NPS AMPP Architecture & Various Netezza appliance models

·         Netezza High Availability Architecture (Clustering, Mirroring, failover)

·         Installing Netezza system and client software

·         Installing Netezza Emulator for day-to-day practice

·         NzAdmin: GUI Admin Tool (Installation & Setup)

·         Netezza Command Line Interface (CLI)

·         Manage NPS with CLI commands

·         Manage User access to Netezza Databases

·         Monitoring Netezza and Linux logs

·         Netezza Events (Setup & Monitoring)

·         Databases & Tables

·         Data Distribution (Hash, Random), Cluster Base Tables, Table Skew

·         Generate Statistics, Zone Maps, Materialized Views, Groom Table

·         Backup & Restore (Host Level, Database Level, Table Level)

·         Database Refreshes & Migrations

·         Netezza Appliance migration (For Example: 6.x to 7.x migration)

·         Data Loading/Unloading using External Tables, NZLOAD, NZ_MIGRATE

·         Data Loading/Unloading using GUI Tools

·         Optimizer and query plans

·         Query history collection & Reporting

·         Netezza Replication/DR Architecture

·         Techniques to improve Netezza performance

·         Frequent DBA activities such as SPU replacements, etc

·         ODBC/JDBC/OLEDB Client Connectivity

·         Working with IBM Netezza Support to resolve issues

 Nisa’s IBM Netezza online course participants will be able to learn:

 ·         In-Database Analytics, a fully scalable and parallelized

·         In-database analytics package

·         R, an open-source statistical language that operates on Netezza

·         Matrix Engine, a parallelized, linear algebra package.

 On completion of Nisa’s IBM Netezza online training, you will be able to:

 ·         Understand how the IBM Netezza architecture and parallel processing capabilities support modelling and analysis paradigms on large-scale data sets.

·         Understand data mining methods in the context of use cases to solve common business problems.

·         Apply new approaches to modelling and analysis made possible by IBM Netezza analytics

·         Use Netezza Analytics data mining methods and statistical functions using the R client or IBM Netezza.

 Nisa training is one of the best platforms to learn all the technologies. Learn your favourite technology from our industry experts. Our trainers have been in the industry for a long time and have been working on real-time projects. Training is provided with a 1:1 ratio. Nisa’s IBM Netezza corporate course study material is provided for their reference. IBM Netezza certification is also provided for the participants. 

 For More information about IBM, Netezza training, feel free to reach us

Name: Albert

Email: albert@nisatrainings.com

 Ph No: +91-9398381825

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6 Weeks Tableau Training in Noida

Scene versus QlikView: Data Visualization

Colossal Data is impacting!

All together for Big Data to tremendously influence the way in which information is spread and separated, data portrayal accept a gigantic activity. There are various affiliations that believe that its real testing to translate data, and this is really where data discernment acts the legend. It is a customary term that depicts an affiliation's undertakings to empower them to comprehend the significance of data by changing over it into visual substance. There are various basic parts, including precedents and examples that consistently go impalpably in substance based data, yet can be successfully observed with data observation. It in like manner helps digest information as warmth maps and rich graphical portrayals.We should push forward in this Tableau versus QlikView blog and difference these gadgets and the going with parameters:

1. Accommodation

QlikView : It is definitely not hard to use and explore the hid examples. To look, basically type any word in any demand into output box for minute and helpful results and it will show affiliations and associations over your data. It is troublesome for customer to structure their very own points of view due to menu driven properties.

Scene: Its interface is essential, not stacked up with such countless at one page and has a streamlined interface. It doesn't offer feature to look for substance over the whole of your data. Customer can without quite a bit of a stretch make their own one of a kind points of view using distinctive things and it is straightforward because of all around arranged GUI interface.

5. Strategy and System Requirement

QlikView: QlikView has its own special data dispersion focus and alternative of scripting feature expands its estimation. We can use stunned layers in QlikView sending. QlikView is viably pass on able and configurable, and starts making stunning reports near foundation. This thing does not use 3D squares; thusly stacks all of the tables and diagrams in memory to engage natural inquiries and arrangement of reports—an advancement not found in various things. It will in general be made on both 32 and 64 bit. Its subsidiary development makes data showing less demanding.Tableau: It doesn't have its very own data dissemination focus. It can not make layers while partner with educational accumulation. It is progressively less complex to send in light of the fact that it requires continuously sorted out data.

3. Cost

QlikView: Its very own discharge is free with limitation of record sharing. Each named customer license is $1,350 and $15,000 for a synchronous customer. Server license is $35,000/server. Additional $21,000/server for PDF allotment organization; $22,500 for SAP NetWeaver connector.

May require RAM refreshes if far reaching amounts of concurrent customers.

Scene: Free Desktop version called "Open" that makes data is available for all to download. Private structures go with fixed charge $999 or $1,999 depending upon data get to. Scene Server – account evidence says $1000/server customer, with least of 10 customers notwithstanding upkeep.

4. Accessibility with Other instruments/Language or Database

QlikView: It arranges with an incredibly wide extent of data sources like Amazon Vectorwise, EC2, and Redshift, Cloudera Hadoop and Impala, CSV, DatStax, Epicor Scala, EMC Green Plum, Hortonworks Hadoop, HP Vertica, IBM DB2, IBM Netezza, Infor Lawson, Informatica Powercenter, MicroStrategy, MS SQL Server, My SQL, ODBC, Par Accel, Sage 500, Salesforce, SAP, SAP Hana, Teradata, and some more. It can connect with R using API joining. It can connect with Big information.Tableau: It can consolidate with an increasingly broad extent of data sources including spreadsheets, CSV, SQL databases, Salesforce, Cloudera Hadoop, Firebird, Google Analytics, Google BigQuery, Hortonworks Hadoop, HP Vertica, MS SQL Server, MySQL, OData, Oracle, Pivotal Greenplum, PostgreSQL, Salesforce, Teradata, and Windows Azure Marketplace. It can connect with R that controls the efficient capacities of the instrument. It can similarly interface with Big data sources.

5. Strategy and System Requirement

QlikView: QlikView has its own one of a kind data dispersion focus and choice of scripting feature builds its estimation. We can use stunned layers in QlikView sending. QlikView is viably pass on able and configurable, and starts making stunning reports near foundation. This thing does not use 3D squares; subsequently stacks all of the tables and diagrams in memory to engage natural inquiries and development of reports—an advancement not found in various things. It will in general be made on both 32 and 64 bit. Its subsidiary development makes data showing less requesting.

Scene: It doesn't have its own data appropriation focus. It can not make layers while partner with educational accumulation. It is progressively less complex to send in light of the fact that it requires logically composed data.

6. Learning Generation

QlikView: Associative advancement makes it even more overwhelming and it examines connection between variables viably. This component now and again help associations to appreciate covered association between data focuses.Tableau: Story prompting feature urges you to make presentation, using your open data centers.

7. Discernment Objects

QlikView: It has extraordinary options available to imagine information. It is stacked with various things. We can play with properties of these articles successfully to alter it. We can in like manner make custom graphs like course, boxplot, geo-spatial layouts by tweaking properties. While embeddings object, it has structure and orchestrating decisions like subject of the report. Here, we need to manage planning choices to make it even more apparently engaging.Tableau: It has extraordinary observation objects with better orchestrating options. It has incredibly extraordinary discernment for geo-spatial portrayals. It has different options of imagining your data. The observations are reliably in the best quality.

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WHY SHOULD YOU LEARN IBM NETEZZA TRAINING?

IBM Netezza online training offers in-depth knowledge of all the essentials of IBM Netezza such as NPS AMPP Architecture & Various Netezza appliance models, Netezza High Availability Architecture like Clustering, Mirroring and Failover. IBM Netezza training also covers Netezza Command Line Interface (CLI), Netezza Events and many more topics like Data Loading/Unloading using GUI Tools etc.

Features of the IBM Netezza Certification training

MaxMunus IBM Netezza online training includes

1. NPS AMPP Architecture & Various Netezza 7.2 appliance models

Netezza High Availability Architecture (Clustering, Mirroring, failover)

 2. Netezza Command Line Interface (CLI)

Manage NPS with CLI commands

 3. Databases & Tables

Data Distribution (Hash, Random), Cluster Base Tables, Table Skew

 4. Netezza Appliance migration (For Example 6.x to 7.x migration)

Data Loading/Unloading using External Tables, NZLOAD, NZ_MIGRATE

 5. Netezza Replication/DR Architecture

Techniques to improve Netezza performance etc.

 Benefits of the MaxMunus’s IBM Netezza course

After the successful completion of Netezza Online Course you will be able to 

1. Understand how the Netezza architecture and parallel processing capabilities supports modeling and analysis paradigms on large scale data sets.

2. Understand data mining methods in the context of use cases to solve common business problems.

3. MaxMunus will provide the best IBM Netezza certification training in collaboration with industry experts with certified trainers.

Top Companies using IBM Netezza

These are some of the top companies who are currently using IBM Netezza

Zurich NA

Salesforce

Kohl’s

USAA

CoStar Group

Zulily

 Salaries offered to IBM Netezza consultants after IBM Netezza certification

Sr. Software Engineer / Developer / Programmer                                 $ 106k

Database Administrator (DBA)                                                                  $ 90k

Systems Analyst                                                                                           $ 58k

Prerequisites for IBM Netezza Corporate course

1. You should be familiar with advanced analytics (statistics, data mining, and so on) in business problem solutions.

2. Working knowledge of R, SQL, or both.

3. Working knowledge of Unix or Linux.

 MaxMunus has successfully conducted 1000+ corporate training in India, Qatar, Saudi Arabia, Oman, Bangladesh, Bahrain, UAE, Egypt, Jordan, Kuwait, Srilanka, Turkey, Thailand, HongKong, Germany, France, Australia and USA.

In IBM Netezza online classes our real time experts will teach to Utilize Netezza Analytics data mining methods and statistical functions using the R client. You will learn to apply new approaches to modelling and analysis by IBM Netezza Analytics.

Conclusion

The demand for IBM Netezza is increasing very high nowadays. Skilled and Certified IBM Netezza developers are getting very high paid jobs. In MaxMunus IBM Netezza Certification course you will be allowed to work on the real-time implementation of Netezza projects which will help you to clear the IBM Netezza Certification Exam. Even our corporate clients have rated us very high for our IBM Netezza corporate training deliveries and have given great feedbacks.

 For more details about IBM Netezza Course feel free to contact

Name- Avishek Priyadarshi

Ph- +918553177744 (call or whatsapp)

Email- avishek@maxmunus.com

Please visit at http://www.maxmunus.com/page/IBM-Netezza-Training

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Fwd: Urgent requirements of below positions

New Post has been published on https://www.hireindian.in/fwd-urgent-requirements-of-below-positions-44/

Fwd: Urgent requirements of below positions

Please find the Job description below, if you are available and interested, please send us your word copy of your resume with following detail to vendor@vinsysinfo.com or please call me on 703-594-5490 to discuss more about this position.

  Software Engineer – Maria DB——àOrlando, FL

Test Engineer—————–àNewark, DE

Business/Data analyst———-àRichmond, VA

Business analyst – data inventory and metadata——–àFoster City, CA

Chatbot AI Developer———–à Jersey City, NJ

ML developer——–àRedmond, WA

    Job Description

Position: Software Engineer – Maria DB

Location: Orlando, FL

Duration: Contract

                                                                                  Job Description:

Responsibilities

Support dynamic requirements and manage relational database management systems (MySQL server, Enterprise MariaDB and MongoDB).

Installation, configuration and upgrading of database management software and related products

Manage patches and software upgrades

Design of physical data storage, maintenance, access, and security administration

Establish and maintain backup and recovery policies and procedures

Monitor and maintain the health of the databases and database server environments

Implement and maintain database security (user management, role management)

Perform database tuning and performance monitoring

Design, Develop and implement databases

Develop Conceptual Data Model, Logical Data Model, Physical Data Model

Support application development teams with database queries, troubleshooting and optimization

Prepare and maintain database documentation and standards and present technical briefings to the customer

Required Skills:

Installation, Configuration and administration of MySQL server, Enterprise Mongo DB and Maria DB

Database Administration on Production Servers with server configuration, monitoring, performance tuning and maintenance with outstanding troubleshooting capabilities

Replication, Backup/Recovery, Disaster recovery and planning.

Batch processes, Import, Export, Backup, Database Monitoring tools and Application support.

Setup of security policies

Logical and physical data base modeling with UML and ERWIN tools

Support for application development teams,

Support for Certification and Accreditation process

Writing SOPs , Operations manuals

Capacity Planning

Experience with MySQL server, MySQL Enterprise, MySQL Workbench, Open Source tools such as Percona Monitoring and Management, Percona toolkit,

Position: Test Engineer

Location: Newark, DE

Duration: Contract (Full time position)

Work Authorization: USC/GC

                                                                            Job Description:

Able to understand the subassembly drawings and perform the assembly

Able to make (build) cable harness based on drawing

Should have extensive experience on usage of manufacturing tools like, Solder station, crimp tools, stripper, cutters, shrink tubes, IC removers, thermal tapes, etc

Maintain lab tools and lab equipment, able to perform preventive maintenance and calibration

Able to disassembly the subsystems

Create manufacturing instructions

Usage of Multimeter, Oscilloscope, counter, power supply, etc

Perform subsystem testing or bench top testing ( Digital  boards and Mixed Signal Boards  )

Vendor interactions, component order, incoming inspection, tool validation

Experience in with Medical domain companies is most preferred

  Technical / Soft Skills

Hand soldering (SMT, through hole)

PCB Layout using Cadence ECAD Tools

Cable harness build and system assembly

Circuit and product testing

Board design process

Medical standard

Component understanding

Electrical troubleshooting

Position: Business/Data analyst

Location: Richmond, VA

Duration: 6+ Months

Mandatory Skills: Data Analyst / Informatica / ETL / Data / SQL Server 

                         ��                                                  Job Description:

Analytical Data Evaluation & Quality Management:

Demonstrates process and technology fluency with analytic applications Demonstrates ability to collect knowledge about information assets: where they are, what format they are in, what level of quality they represent, and their value to the enterprise. Specifically: Search & Discover

Demonstrates understanding of the available data within PFG, how it is used, and the relationships between various data objects.

Demonstrates an understanding of secondary data sources, such as those from data providers Model: Understands various model entities, attributes, relationships, and keys Provides input to data modeler for model design changes to reflect changes in business requirements Basic/Advanced Profiling:

Assesses the shape of information assets to understand levels of quality

Creates and updates data profiles

Proactively test against a set of defined (or discovered) business and quality rules to distinguish those records that conform to PFG’s defined data quality expectations and those that don’t; tune rules as necessary

Conducts single column and multi-column analysis

Single column analysis: e.g. detect data format errors, valid set errors, valid range errors, null/non null errors, primary key validation, minimum/maximum/average string length, cardinalities, pattern & data types, values distributions

Multiple column analysis: e.g. foreign key discovery, inclusion dependencies, functional dependencies, partial and conditional dependencies

Provide extensions to basic profiling with detailed analysis of data objects including join & redundancy analysis, relationship inference and domain discovery as required

Conduct data verifications during builds and data validations as required by the business Performs liaison role between business process owners and development teams to verify results are reconciled for approval of conversions

Executes validations as required to ensure data is fit for intended business analytics purpose Execute and/or coordinate rigorous testing activities as required

Update metadata info/business glossary as required

Monitors and shares data quality metrics with scorecards and reports to track data quality

Business Engagement:

Business Requirements Gathering: Demonstrates ability to engage within a collaborative process, bringing stakeholders together to establish which business outcomes are desired. Ensure consistent, comprehensive and actionable understanding of analytic informational needs: Demonstrates ability to research business problems and create models that help analyze business problems

Demonstrates business process understanding

Understands business rules applied to data

Demonstrates skills to train users to use information correctly; oversees training activities for business resources new to analysis tools

Supports end-user ‘self-service’ strategy and the development of business super user teams Demonstrates a high proficiency in business facing technology tools as required: Informatica EDM, IBM Netezza, IBM Cognos, Microsoft SSRS

Enables stakeholders to collaborate, build, and manage a common business vocabulary across the organization

Supports the establishment of a common view of business value for the BICC organization Collaborates with data stewards and developers during initial data profiling activities and for resolution of data quality

Position: Business analyst – data inventory and metadata

Location: Foster City, CA

Duration: 6 Months

                                                                                   Job Description:

Please look for a resource to perform DQ and Metadata requirement gathering, analysis, documentation with the following skills:

1.       Strong data analysis skills – previous report development experience is plus

2.       Oracle EBS, OBIEE background is a plus

3.       Requirement gathering and analysis with process flow diagraming experience

4.       Understanding of metadata management data quality management

5.       Tableau background and experience

Primary responsibility:

Data Asset and Inventory Gathering

Data Inventory Analysis

Dashboard development – define metrics to measure data landscape maturity

Data Inventory management policy and requirement gathering

Requirement documentation

Position: Chatbot AI Developer

Location: Jersey City, NJ

Duration: Contract

Experience: 8+ years

                                                                                   Job Description:

Chatbot AI Developer in Kore.ai Framework. (Min 6 years of experience or more)

Responsibilities would be more of automation of some Production Support tasks using Non-Conversational Chatbot AI….

Position: ML developer

Location: Redmond, WA

Duration: 6 Months

  Job Description:

  Develop deep learning-based real-time object detection from Video stream computer vision application.

Develop and Train Image detection Models using frameworks like PyTorch, FastAI, Tensor Flow and similar frameworks.

Develop Image Detection algorithms using the trained models and system integration using Python.

  Thanks, Steve Hunt Talent Acquisition Team – North America Vinsys Information Technology Inc SBA 8(a) Certified, MBE/DBE/EDGE Certified Virginia Department of Minority Business Enterprise(SWAM) 703-594-5490 www.vinsysinfo.com

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Data processing technologies are developing as rapidly as data collection is advancing, that is, at a continually accelerating rate. There’s a whole lot of technology that is breaking ground and offering new solutions in this exciting field.

Let’s take a look at what some of the latest cutting-edge technologies are for data processing systems.

DISTRIBUTED SYSTEMS ARCHITECTURE

Big data sets common in data processing today have limitations on computational power. The technology needed to deal with this is called distributed systems architecture.

MPP – Massive parallel processing, and Hadoop are two key technologies that are leading the industry in distributed systems architecture. Both feature the “shared nothing” technology that ensures autonomous operation.

The key difference between the two is that MPP is proprietary and rather costly to implement, while Hadoop is open source and can be integrated from very small, low cost applications, to very very large ones. While Hadoop is more recent than MPP, and allows flexibility and scalability, MPP remains slightly quicker.

MPP systems are provided by Teradata, Netezza, Vertica, and Greenplum. Oracle and Microsoft also have their own MPP systems.

Hadoop is a software project by Apache, containing a collection of software utilities that provide huge storage and processing power. Hadoop uses MapReduce to process large non-structured data sets, as the name implies, by a map function, and a reduce function within Hadoop. Many platforms can be built on top of the Hadoop framework. Non-proprietary applications available for use on Hadoop continue to develop in number and complexity.

QUERY OPTIMIZATION

Part of leading technology for data processing in a relational database is query optimization design. Query optimization is an automated process that attempts to provide the best possible answers based on a range of possible query plans. A query plan is a set of rules that a relational database uses to search data for the required parameters. Query optimization can effectively determine which searches are valid, and which will be most accurate, efficient, and timely.

Query hints may be built into query optimization, for example, a query on a GPS database might be selected for the fastest or the quickest route. A simplified example of query optimization is to imagine a query for the number of a certain car make and model, where the database could search all makes then all models, just all models, since the model subset automatically includes make. Query optimization would choose the latter.

NON-RELATIONAL DATABASES – NO-SQL

With the explosion of Big-Data, has come two more players in data processing technology, non structured and dark data.

Traditional databases have relational structure, usually called relational data base management systems (RDBMS), and are primarily built on SQL – structured query language, which is why non-relationship databases are coined No-SQL.

A Non-relational, No-SQL database can store and access un-structured data easily using a common data format called JSON documents, and can import JSON, CSV, and TSV formats.

A JSON, Javascript Object Notation is a lightweight data-interchange format, simple yet very powerful, since stored data need not be structured. The ability to store and access this non-structured data is what makes non-relational databases such important technology for data analytics systems. As a draw back, since they are non relational, the query itself has to draw a relation, so working with a non-relational database requires more skill.

Popular No-SQL databases used in data processing are MongoDB, Arango DB, Apache Ignite, and Cassandra.

DATA VIRTUALIZATION

Data storage and retrieval can sometimes deteriorate data due to the format that is required by the storage or retrieval. Unlike the traditional ETL (extract, transform, load) data method, in data virtualization the data remains where it is, a viewer accesses it in real time, from it’s existing location, solving the problem of format losses. An abstraction layer between viewer and source means that the data can be used without extraction and transformation.

A simplified example of data virtualization we can all identify with is the technology that drives images on social media. When you view an image on most social media platforms, normally you’re viewing it temporarily in real time on your mobile device or computer, but it exists in reality on the server of whichever social media you’re on. The file format is not relevant, nor do you need software related to the format to view it. The image is only converted into real data if it’s downloaded or via a screenshot, but the data is searchable and viewable without ever opening the file itself because of data virtualization.

STREAM PROCESSING AND STREAM ANALYTICS

Stream processing provides the capability for performing actions and analyzing events on real-time data. To do this stream processing makes use of a series of continuous queries. Stream processing allows data information to be processed before it lands in a database, which makes it incredibly powerful.

A good example to explain the process of live stream data analytics is the correlation of GPS data or driver mobile data with user locations. Uber’s apps have used this with great success to revolutionize private transport. Many bank applications also use stream processing to immediately alert users of suspicious activity.

Striim, IBM Infosphere, SQLStream, and Apache Spark are examples of common streaming database applications.

DATA MINING AND SCRAPING

Data mining and scraping technology is improving the content that data-processing systems have available in the data capture phase. Data mining in it’s simplest form essentially takes very large sets of data and extracts smaller more useful sets. Data mining software automizes the fundamental data processing function of finding patterns in large data sets, to create smaller subsets which match search query criteria. Web search is essentially a form of data mining we all use, taking the catalogue of websites and extracting only those that match search terms. Data mining may be applied to any type of data, text, audio, video, images. Data mining can be incredibly useful in finding information a company doesn’t currently have from large unstructured data sources.

Scraping is similar to mining, but where mining analyzes data for patterns, scraping collects data matching certain parameters.

MACHINE LEARNING AND AI

Data processing is a key field for advances in machine learning and AI. Data preparation involves cleaning and transforming the data for us. It often takes around 60 to 80% of the whole data processing time, with as little as 20% for analytics and presentation. The preparation of data is largely repetitive and time consuming, so it is a perfect area for implementation of the latest technology in machine learning. Processing large amounts of data, especially when complex text based data like searching contracts, reports, articles, machine learning is a one of the latest technological advancements that will improve the industry. Machine learning can match phrases in a range of documents based on connections that previously only humans could do. We think of AI and machine learning as way out there, but we actually interact with it every day on platforms like Google search. Haven’t you noticed how it seems to know more and more what you might be thinking, with scary accuracy? It’s a simple concept yet, currently one of the most extensive examples of machine learning data processing in everyday use. Machine learning is also growing steadily in user interaction devices on the web. Automated answers to users questions, along with databasing questions and responses for improved machine learning, helps organizations better serve their customers.

AI and machine intelligence is advancing faster than we can train people to work with it. An unbelievable 2 jobs are available for every AI graduate in the UK.

DATA COMPRESSION

Compression is driving data processing, with larger and larger data sets, any reduction in data sizes will improve experiences. Storage space and processing times can be reduced significantly with better compressions techniques, this in turn significantly reduces costs and improves performance. Facebook has released their latest compression tool Z standard on an open source platform. While previous storage compression devices had around 9 levels, Z standard has 22 levels. Data compression will help improve our storage and processing capacities.

SELF-DRIVING DATABASE MANAGEMENT SYSTEMS

The last and most significant technology in data processing systems is the self-driving database management system. A self-driving database can be run without user intervention, and totally managed by the user. Leading this technological advancement is Oracle’s Autonomous Database. Oracle’s founder claims it will revolutionize data management, since there is no need to apply patches, complete manual back-ups, or tune, it’s capable of total automation. Peleton is a good example of a leading open source autonomous database solution.

For data processing, it’s important to stay ahead of the trends. Check out some of the ideas we’ve discussed here to find out more about where your data processing systems can evolve.

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