BIG DATA PROCESSING 2017-11-07T13:23:24+00:00

Big Data Processing

Evening Bootcamp, Big Data Processing

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Big Data Processing

Big Data Processing

Objective of Big Data Processing Course

The objective of this Big Data Processing course is to introduce Big Data Processing Fundamentals.  This Big Data Processing course focuses on fundamental theory. You will receive a full training in Big Data Processing. This course guarantees you that you will receive all tools end theory needed from experts in the field.

This Big Data Processing course takes students through the fundamentals, giving them a solid foundation that they can build upon, then moves on to more advanced knowledge, teaching them how they can apply Big Data Processing in practical situations.

TRAINING METHODOLOGY

In Class: $2,999
Locations: NEW YORK CITY, D.C, BAY AREA.
Next Session: 25th Nov 2017

Online: $1499
Next Session: On Demand

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Home / All courses /Evening Bootcamp/Big Data Processing

BIG DATA PROCESSING

Instructor: John Doe, Lamar George

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DESCRIPTION

Big DATA Processing

Big DATA Processing

BIG DATA PROCESSING

Data ingestion is the process of obtaining and importing data (which can be used immediately or stored in a database). Indeed, “ingest” means that the data is taken.

Furthermore, the data (for Big Data Processing) is streamed in real-time or ingested in batches. In the case the data is ingested in real time, each data item is imported as it is emitted by the source. In the case the data is ingested in batches, data items are imported in discrete chunks (small batches) at periodic intervals of time.  Indeed, the data ingestion process starts by prioritizing data sources, validating individual files and routing data items to the correct destination.

In the case there are many big data sources that exist in diverse formats, it can be difficult to ingest data fast and process it efficiently. In the case the data ingestion is automated, the software (used for carrying out the process) could have data preparation features (to structure and organize data), so it can be analyzed automatically.

“Big Data” is so popular and the open source community has released tools and frameworks to store, process, and analyze data that couldn’t fit or the data is unsuitable for databases.

Actually, Big Data, Internet of Things and social media have challenged the approach to data management. Indeed, the old centralized architecture data systems no longer meet companies that need it because the large volumes of distributed data arriving at such a high velocity.

Actually, as the amount of data grows, so does the complexity of companies’ IT landscapes. Indeed, dealing with structured and unstructured data from multiple applications, files, databases, data warehouses, and data lakes is difficult to handle it. Furthermore, existing tools and technologies can’t handle data so easily and it is time-consuming.

Actually, developers, data scientists, business warehouse administrators, and business analysts have their own requirements, approaches, and tools.

Big data have an engineering complexity, but Big Data is a great opportunity. The advantage of Big Data is that it can be used for decision making, new or improved business processes, and models, and, have customer satisfaction. Big data is used for efficiency gains and increased profits, but Big Data also has a major impact on society. The advantages of Big Data Processing are: enabling smarter cities to save lives (by anticipating natural disasters), driving medical research. Big Data Processing is used for storage, analysis, sharing, and more.

There are many open source Big Data tools, but they lack the essential life-cycle management, governance, and security capabilities that are required by companies. Actually, managing multiple data formats and silos is still a huge challenge. Indeed, the ability to connect just one enterprise’s database with Big Data would provide enormous business value. Furthermore, complex and expensive workarounds to integrate the many different technologies and systems is the only way that enterprises are managing the data.

A unified and open approach to help enterprises accelerate and expand the flow of data across the data landscapes for all users is something enterprises still researching.

Enterprises have extensive data integration, data orchestration, and data governance capabilities. Big data processing is the solution for providing customers with the same high standard of data access, quality, and enrichment when processing their Big Data as they do with their enterprise data.

Big Data Processing focus on enabling users to create agile, data-driven applications and processes that can respond to changes and anomalies in the data in real time.

For example, the best container scheduler is Kubernetes that help to orchestrate fleets of operations and processes into scalable pipelines, making it possible to effortlessly scale thousands of processing nodes to cope with high volumes of data. If it is Google cloud, on-premise, in a data lake, a data warehouse, or application, business and IT users must be able to get the information they need wherever the data is saved.

Actually, the digital age requires companies to find the right balance between stability and agility (which means that are implemented new use cases and it is ensured that existing processes are not disturbed). It is about having the greatest efficiency and winning with new technologies and business models. New technologies are used to go further.

Kafka (Big Data Processing) is used by one-third of all Fortune 500 companies.

Apache Kafka enables big data analytics opportunities by providing a high-scale, low-latency platform for ingesting and processing live data streams, which is called Big Data Processing or big data processing. Indeed, valuable data being managed in a variety of databases, enterprises can benefit from ingesting (Big Data Processing) that data through Kafka to support their analytics or Data Lake initiatives.

This real-time data ingestion can be challenging due to the potential impact on source systems, complexity in custom development and ability to efficiently scale to support a large number of data sources.

Furthermore, Flume, Kafka, and NiFi are Apache tools used for Big Data Processing, which deals with huge volume, variety, and velocity of data showing up at the gates of what would typically be a Hadoop ecosystem.

Kafka, Kinesis, SQS are Evaluating Message Brokers. Actually, they are some of the best message brokers for big data applications.

Companies such as Netflix, Uber, and Yelp have huge experiences with Kafka (Big Data Processing).

Indeed, LinkedIn announces an open-source tool to keep Kafka (Big Data Processing) clusters running. Actually, LinkedIn now has a load-balancing tool that recognizes when clusters are about to break, which is called Cruise Control (and it is open-source and helps keep Kafka clusters up and running).

Informatica enables enterprise-scalable hybrid and multi-cloud deployments across leading cloud ecosystems, including Salesforce, AWS, and Microsoft Azure.

Informatica is to make enterprises agiler and lead their own intelligent disruptions using data has never been stronger. It is used to accelerate their data-driven digital transformation, and enterprises need to manage data using a strategic approach. Informatica (big data digestion) has reimagined data management, powered by metadata-driven Artificial Intelligence (AI).

Informatica is the Enterprise Cloud Data Management leader. Informatica is an intelligent, scalable and integrated platform for managing any data to accelerate data-driven digital transformation. Informatica’s engine has enterprise-wide and metadata-driven AI at its core and informs the Informatica Intelligent Data Platform, from data cataloging and discovery to data governance stewardship to big data and data lake management. The resulting business outcomes include:

Informatica is a faster and complete business insights that are leveraging a scalable and flexible intelligent data platform, which was built to manage data of any volume, velocity, and complexity leveraging best in class big data technologies.

Informatica is a faster hybrid and multi-cloud data management deployments across ecosystems, leading to greater ROI and decreased risk of moving to the cloud.

Furthermore, digital transformations are data-driven and require a strategic approach to data management that catalogs and governs all data that matters, secure data that needs protection, ensures the quality of trusted data, scales for big data workloads and real-time processing across hybrid architectures, and brings it together for a complete view of all an organization’s data.

Informatica (as the leader in Enterprise Cloud Data Management) delivers in spite of all these challenges using an intelligent data platform (powered by CLAIRE).

Furthermore, Informatica delivers the industry’s leading AI-driven enterprise data catalog. Furthermore, Informatica Enterprise Information Catalog can discover and catalog all types of data and data relationships across the enterprise using an AI-driven metadata management.

Only Informatica (Big Data Processing) manages all types of data at all latencies, including real-time streaming, for all types of users, at scale.

Actually, only Informatica manages enterprise-scalable hybrid and multi-cloud deployments with comprehensive Hadoop ecosystem.

Big Data is used to gain that competitive edge in the market.

The data has also become too complex and dynamic to be stored, processed, analyzed and managed with traditional data tools. Actually, Big Data is now analyzed by computer systems to find trends, patterns, and associations (such as human behavior and interactions). Big data is used for decision-making using techniques such as predictive analytics, user-behavior analytics, etc.

Companies using Big Data

  • Companies use big-data-ingestion tools to extract useful insights from large data sets such as surveys, statistics and case studies.
  • Flipkart, Amazon and other online e-commerce sites use big-data techniques to study the behavior of their customers and help them get what they want.
  • Facebook use different Big Data techniques and keeps track of different user actions, photo uploads, comments, shares, etc, using these techniques.
  • MNCs (such as Walmart) use Big Data techniques to improve their “employee intelligence quotient” and “customer emotional intelligence quotient”.
  • Family restaurants like Dominos, McDonald’s and KFC use predictive and user-behavior analytics to increase the efficiency of their marketing and continuously improve the customer experience.

Different factors when the user works with Big Data

  • Big Data Processing is a process for moving data (especially unstructured data) from where it originated, into a system where it can be stored and analyzed. Big Data Processing can be continuous or asynchronous, in real-time or batched, or even both.
  • Harmonisation deals with the improvement of data quality and its use with the help of different machine learning capabilities. Indeed, Harmonisation also interprets the characteristics of the data and the different actions taken on it, subsequently using that analysis to improve data quality.
  • Analysis deals with the analysis of the data sets in order to understand the behavior of data and find patterns or trends.
  • Visualisation is the process of presenting the data in a graphical format, which helps decision makers to get concepts or identify new patterns in the data set.
  • Democratisation is the ability for specific information in a digital format to be accessible to the end user.
  • A/B testing is a method of comparing the two versions of an application to determine which one performs better, which is also called split testing or bucket testing. Indeed, A/B testing is used to optimize a conversion rate by measuring the performance of the treatment against that of the control.
  • Association rule learning is rule-based machine learning used to discover interesting relationships between variables in large databases. Indeed, Association rule learning uses a set of techniques for discovering the interesting relationships, also called ‘association rules’, among different variables present in large databases.
  • Natural language processing is a field of computational linguistics and artificial intelligence concerned with the interactions between computers and human languages and it is used to program computers to process large natural language corpora. Research has focused more on the statistical models, which make soft and probabilistic decisions based on attaching the real-value weights to each input feature. The edge that such models have is that they can express the relative certainty of more than one different possible answer rather than only one, hence producing more reliable results as compared to when such a model is included as one of the components of a larger system.

The personal computer gaming industry and big-data analytics are working together.

The $18.4 billion market of the gaming industry is growing. Actually, the industry is beginning a massive move to the Internet.

Furthermore, ZeniMax Media Inc. is the company that is leading this trend.

Indeed, development teams worked independently of each other and chose their own story lines and business metrics. Actually, if game developers don’t keep their audience’s attention, players quickly go elsewhere.

Then it is used big data to increase gaming industry market. Furthermore, the new business model demands that developers monitor how the games are being played.

Actually, games generate a huge amount of data and it is loaded into its database.

Indeed, getting to an integrated data model was a challenge. Actually, in the past, companies siloed organizational structure and it gave developers little reason or incentive to share data. Indeed, each team selected its own metrics and made its own decisions. Nowadays, Big Data Processing tools are used for it.

It is demanded a unified approach for online integration. The game company needed to track the activity of individual players across different games and forums. Furthermore, they need to know where and what players buy.

Game companies have a consolidation process, which unifies the games into a single service in a data warehouse. Then, they create a single view of disparate data. This was about transforming data from a jumble of formats into a single stream that could be loaded into the warehouse.

Using Python scripts, the data was transformed. The problem was that the process didn’t scale well, and finding programmers with the right skills was a problem. ZeniMax uses a solution (Big Data Processing) that would serve as a long-term connector between the data lake and the warehouse.

Actually, there are big-data solutions (Big Data Processing) that can do the ingestion, and there are data warehousing solutions that provide a good mechanism for delivering a view that’s good for the company. The problem was how to glue together the data lake and data warehouse into a single pipeline.

Pentaho(Big Data Processing) was used to unify data. Indeed, other integration products (Big Data Processing) are Talend SA, Informatica Corp., Pentaho Corp. (now Hitachi Vantara), ZeniMax selected Pentaho’s Data Integration and Analytics Platform. Pentaho (Big Data Processing) provides both data transformation and analytical modeling features.

Raw data can be loaded and transformed in an Amazon Web Services (AWS) Inc. Redshift data warehouse, with all modeling process taking place in Pentaho (Big Data Processing). Indeed, the integration solution (Big Data Processing) unified two existing data lakes into one. Indeed, the entire data integration process (Big Data Processing) is now visible in a single place.

Pentaho (Big Data Processing) automated much of the work that had previously been done manually or with Python scripts.  It was reduced the overall level of effort.

Actually, analysts are now able to spend their time digging into gamer behavior instead of loading data. Using Big Data Processing, developers can now fine-tune players’ experiences more quickly.

Using Big Data Processing, analysts can determine that players who lose a certain number of consecutive matches are likely to quit. Indeed, the developers (using Big Data Processing) can now react and prepare for that.

Big Data Processing also improves a gamer’s chance of success. Indeed, the gaming company can test ideas and get behavioral feedback in near real-time.

The bottom line for ZeniMax(Big Data Processing solution) improved their success using Big Data Processing.

A problem in big data is the data-drift problem, which has emerged as a critical technical challenge for data scientists and engineers in unleashing the power of data. Data-drift problem delays businesses from gaining real-time actionable business insights and making more informed business decisions.

StreamSets is a big data solution for this data-drift problem. It is not only used for Big Data Processing but also for analyzing real-time streaming data. StreamSets (Big Data Processing solution) is used to identify null or bad data in source data and filter out the bad data from the source data in order to get precise results. StreamSets (Big Data Processing solution) also helps the businesses in making quick and accurate decisions.

“Big data” is growing with structured data in corporate databases and data warehouses as well as a wide range of semi-structured data (such as web server logs and sensor logs) and unstructured data (including documents, email, and image files). The problem is the big-data gestion. Big Data Processing entails bringing together data from a range of source systems into a big-data-computing platform such as Hadoop so that it can be mined and analyzed for insights into how the business can operate more effectively and profitably.

Big Data Integration (Big Data Processing) with Attunity Replicate.

Big Data Processing requires an interface with a variety of source and destination system technologies. Actually, some organizations find themselves using multiple data movement tools that apply different processes to different platforms. And more and more businesses are turning to a better solution (Big Data Processing), which is Attunity Replicate. Attunity Replicate (Big Data Processing solution) is an enterprise data integration (Big Data Processing) platform that makes it easy to create and run Big Data Processing processes without any need for manual coding or deep technical knowledge of the source or destination system interfaces. It is a single unified solution for Big Data Processing. Actually, Attunity Replicate (Big Data Processing solution) saves money and speeds time-to-value for big data initiatives. Attunity Replicate (Big Data Processing solution) reduces dependence on ETL programmers and simplifies the administrative aspects of big data integration (Big Data Processing).

Big Data Integration (Big Data Processing) from Nearly Any Source System

Attunity Replicate (Big Data Processing) delivers the industry’s broadest coverage for big data integration (Big Data Processing) from diverse source systems. Attunity Replicate (Big Data Processing) is used to ingest data from nearly any type of source including such as the following:

  • Relational databases: SQL Server, Oracle, MySQL, Sybase, DB2, Informix, and more
  • Data warehouses: Netezza, Teradata, Exadata, Vertica, and more
  • SAP applications: ERP, CRM, SCM, and more
  • Legacy mainframe systems: IMS/DB, VSAM, and more
  • Semi-structured and unstructured content in file systems
  • Cloud sources: Salesforce, Amazon RDS, and more

Attunity Replicate (Big Data Processing) is as universal on the destination side as it is on the source side, making it fast and easy to load bulk and real-time data into big data (Big Data Processing) targets including:

  • Hadoop distributions
  • Database systems and data warehouse systems
  • Cloud-based targets like Amazon S3, Elastic Map Reduce, or Redshift

For real-time data integration (Big Data Processing) in big data environments, Attunity (for Big Data Processing) supports data streaming through Apache Kafka. With Attunity’s (for Big Data Processing) native support for change data capture and Kafka-compliant message encoding, you can easily configure, execute and monitor Kafka streams to NoSQL systems such as Cassandra or MongoDB.

Bigdataguys has organized courses to help developers gain a greater understanding of Big Data Processing. This course gives you excellent opportunities in the job market. These classes aim to bring students up to speed on Big Data Processing.

This Big Data Processing course of Bigdataguys offers you to know more about Big Data Processing. The best way to learn about Big Data Processing is to take a course with us. This Big Data Processing course covers the basic theory.

The average salary for “Big Data Processing engineer” ranges from approximately $94,765 per year for Business Intelligence Developer to $116,446 per year for Senior Java Developer.

CURRICULUM

Big Data Ingestion – Lecture1.1 How the internet works (Ports, DNS, Browser/HTTP Client)
Big Data Ingestion – Lecture1.2 RPC/Web services vs normal browser usage
Big Data Ingestion – Lecture1.3 Hypertext Transfer Protocol
Lecture1.4 JSON & XML
Lecture1.5 VirtualEnv & Github

Lecture2.1 Resources and elements of REST
Lecture2.2 Richardson Maturity Model
Lecture2.3 Common API features

Lecture3.1 Key Python modules (requests.py, JSON, etc.)
Lecture3.2 Using client libraries
Lecture3.3 OAuth and Basic Access Authentication
Lecture3.4 Working code

Lecture4.1 Scaling your ingestion pipeline
Lecture4.2 Data ingestion best practices
Lecture4.3 Storing your data
Lecture4.4 A sample ingestion pipeline

Online: $1499
Next Batch: On Demand

In Class: $2999
Locations: New York City, D.C., Bay Area
Next Batch: starts from 25th Nov  2017

COURSE HIGHLIGHTS

Skill level: Intermediate
Language: English
Certificate: No
Assessments: Self
Prerequisites: Basic Python programming

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Advanced Course like Big Data Processing  duration largely depends on trainee requirements, it is always recommended to consult one of our advisors for specific course duration.

We record each LIVE class session you undergo through and we will share the recordings of each session/class.

If you have any queries you can contact our 24/7 dedicated support to raise a ticket. We provide you email support and solution to your queries. If the query is not resolved by email we can arrange for a one-on-one session with our trainers.

You will work on real world projects wherein you can apply your knowledge and skills that you acquired through our training. We have multiple projects that thoroughly test your skills and knowledge of various aspect and components making you perfectly industry-ready.

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Yes. All the training sessions are LIVE Online Streaming using either through WebEx or GoToMeeting, thus promoting one-on-one trainer student Interaction.

The Big Data Processing  by BigdataGuys will not only increase your CV potential but will offer you a global exposure with enormous growth potential

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INSTRUCTORS

John Doe
Learning Scientist & Master Trainer 
John Doe has been a professional educator
for the past 20 years. He’s taught, tutored,
and coached over 1000 students, and he
holds degrees in Physics and Literature
from Northwestern University. He has
spent the last 4 years studying how
people learn to code and develop applications.

Lamar George
Learning Scientist & Master Trainer 
He has been a professional educator for
the past 20 years. He’s taught, tutored,
and coached over 1000 students, and
he holds degrees in Physics and Literature
from Northwestern University. He has
spentthe last 4 years studying how
people learn to code and develop applications.

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