The term Big knowledge refers associate to any or all the information that’s being generated across the world at an new rate. This knowledge might be either structured or unstructured. Today’s business enterprises owe a large a part of their success to Associate in Nursing economy that’s firmly knowledge-oriented.
Data drives the trendy organizations of the globe and therefore creating sense of this knowledge and unraveling the assorted patterns and revealing unseen connections inside the huge ocean of information becomes essential and a massively bounties endeavor so. there’s a desire to convert huge knowledge into Business Intelligence that enterprises will promptly deploy. higher knowledge ends up in higher higher cognitive process Associate in Nursing an improved thanks to strategist for organizations notwithstanding their size, geography, market share, client segmentation and such different categorizations. Hadoop is that the platform of selection for operating with very massive volumes of information.
The most flourishing enterprises of tomorrow are those which will add up of all that knowledge at very high volumes and speeds so as to capture newer markets and client base.
Big knowledge has bound characteristics and therefore is outlined victimization 4Vs namely:
Volume: the number of information that companies will collect is actually huge and therefore the amount of the information becomes a important consider massive knowledge analytics.
Velocity: the speed at that new knowledge is being generated all because of our dependence on the net, sensors, machine-to-machine knowledge is additionally vital to analyze massive knowledge in a very timely manner.
Variety: the information that's generated is totally heterogeneous within the sense that it might be in numerous formats like video, text, database, numeric, detector knowledge so on and therefore understanding the sort of massive knowledge could be a key issue to unlocking its worth.
Veracity: knowing whether or not the information that's offered is returning from a reputable supply is of utmost importance before deciphering and implementing massive knowledge for business desires.
Viscosity: refers to the inertia when navigating through a data collection. For example due to the variety of data sources, the velocity of data flows and the complexity of the required processing. Virility – measures the speed at which data can spread through a network.
Virility: measures the speed at which data can spread through a network. Veracity – relates to the quality and origin of the data to determine whether it is trustworthy, conflicting or impure. Value – refers to the value that could be extracted from certain data and how Big Data techniques could increase this value.
Value: When we talk about value, we’re referring to the worth of the data being extracted. Having endless amounts of data is one thing, but unless it can be turned into value it is useless. While there is a clear link between data and insights, this does not always mean there is value in Big Data. The most important part of embarking on a big data initiative is to understand the costs and benefits of collecting and analyzing the data to ensure that ultimately the data that is reaped can be monetized.
Visibility – the state of being able to see or be seen – is implied. Data from disparate sources need to be stitched together where they are visible to the technology stack making up Big Data.
o Lesson 1 Introduction to the course
o 1.1 Introduction to the course
o 1.2 Access to the practice laboratory
o Lesson 2 Introduction to Big Data and Hadoop
o 1.1 Introduction to Big Data and Hadoop
o 1.2 Introduction to Big Data
o 1.3 Big Data Analysis
o 1.4 What is Big Data?
o 1.5 Four Big Data Vs
o 1.6 Case study of the Royal Bank of Scotland
o 1.7 Challenges of the traditional system.
o 1.8 Distributed systems
o 1.9 Introduction to Hadoop
o 1.10 Components of the Hadoop ecosystem, part one
o 1.11 Components of the Hadoop ecosystem, part two
o 1.12 Components of the Hadoop ecosystem, part three
o 1.13 Commercial distributions of Hadoop
o 1.14 Demo: Simpleness Cloud lab Tutorial
o 1.15 Themes
o Verification of knowledge
o Lesson 3 Hadoop architecture, spread storage (HDFS) and YARN
o 2.1 Distributed storage of Hadoop Architecture (HDFS) and YARN
o 2.2 What is HDFS?
o 2.3 Need for HDFS
o 2.4 Regular vs. HDFS file system
o 2.5 HDFS features
o 2.6 HDFS architecture and components
o 2.7 High availability cluster implementations
o 2.8 Namespace of the HDFS component file system
o 2.9 Data block division
o 2.10 Data Replication Topology
o 2.11 HDFS command line
o 2.12 Demo: Common HDFS Commands
o HDFS command line
o 2.13 Introduction of FIO
o 2.14 HILO use case
o 2.15 HILO and its architecture
o 2.16 Resource Manager
o 2.17 How Resource Manager works
o 2.18 Application Master
o 2.19 How YARN runs an application
o 2.20 Tools for YARN developers
o 2.21 Demo: tutorial of the first part of the cluster
o 2.22 Demo: tutorial part two of the cluster
o 2.23 Main topics
o Verification of knowledge
o Hadoop, distributed storage (HDFS) and YARN Lesson
4 Data ingestion in Big Data and ETL systems
o 3.1 Data ingestion in Big Data and ETL systems
o 3.2 Summary of data collection, part one
o 3.3 General description of data entry, part two
o 3.4 Apache Sqoop
o 3.5 Sqoop and its uses
o 3.6 Sqoop Processing
o 3.7 Sqoop import process
o 3.8 Square connectors
o 3.9 Demo: import and export data from MySQL to HDFS
o Apache Sqoop
o 3.9 Apache Channel
o 3.10 Channel Template
o 3.11 Channel scalability
o 3.12 Components in the channel architecture
o 3.13 Channel component configuration
o 3.15 Demo: Twitter data intake
o 3.14 Apache Kafka
o 3.15 Adding user activity using Kafka
o 3.16 Kafka data model
o 3.17 Partitions
o 3.18 Apache Kafka Architecture
o 3.21 Demo: Kafka Cluster configuration
o 3.19 Example of producer side API
o 3.20 Consumer side API
o 3.21 Example of consumer side API
o 3.22 Kafka Connect
o 3.26 Demonstration: creation of a Kafka data pipeline sample with producer and consumer
o 3.23 Main topics
o Verification of knowledge
o Data ingestion in Big Data and ETL systems
Lesson 5 Distributed Processing - MapReduce Framework and Pig
o 4.1 MapReduce and Pig distributed processing framework
o 4.2 Distributed processing in MapReduce
o 4.3 Word count example
o 4.4 Map execution phases
o 4.5 Distributed map execution environment of two nodes
o 4.6 MapReduce Jobs
o 4.7 Interaction of work at work Hadoop MapReduce
o 4.8 Setting up the environment for MapReduce development
o 4.9 Class set
o 4.10 Create a new project
o 4.11 Advanced MapReduce
o 4.12 Types of data in Hadoop
o 4.13 OutputFormats in MapReduce
o 4.14 Use of distributed cache
o 4.15 MapReduce Unions
o 4.16 Replicated Union
o 4.17 Introduction to pork
o 4.18 Pork components
o 4.19 Pig data model
o 4.20 Interactive pig modes
o 4.21 Swine operations
o 4.22 Various relationships with developers
o 4.23 Demo: analysis of web log data using MapReduce
o 4.24 Demonstration: analysis of sales data and KPI resolution using PIG
o Apache pig
o 4.25 Demo: Wordcount
o 4.23 Main topics
o Verification of knowledge
o Distributed processing: MapReduce Framework and Pig
Apache Hive Lesson 6
o 5.1 Apache Hive
o 5.2 Hive SQL on Hadoop MapReduce
o 5.3 Hive architecture
o 5.4 Interfaces to execute Hive queries
o 5.5 Running Beeline from the command line
o 5.6 Hive Metastore
o 5.7 Hive DDL and DML
o 5.8 Create new table
o 5.9 Data types
o 5.10 Data validation
o 5.11 File format types
o 5.12 Data serialization
o 5.13 Avro table and section diagram
o 5.14 Sampling hive and buckle optimization partitioning
o 5.15 Unpartitioned table
o 5.16 Data entry
o 5.17 Dynamic partitioning in Hive
o 5.18 Bracketing
o 5.19 What do the cubes do?
o 5.20 Hive Analytics UDF and UDAF
o 5.21 Other functions of the hive
o 5.22 Demonstration: real-time analysis and data filtering
o 5.23 Demonstration: real world problem
o 5.24 Demonstration: data representation and import with Hive
o 5.25 Topics
o Verification of knowledge
o Apache hive
o Lesson 7: NoSQL databases - HBase
o 6.1 NoSQL HBase databases
o 6.2 Introduction to NoSQL
o Demonstration: cable adjustment
o 6.3 HBase Overview
o 6.4 HBase Architecture
o 6.5 Data model
o 6.6 Connection to HBase
o HBase Shell
o 6.7 Main topics
o Verification of knowledge
NoSQL databases - HBaseLesson
8 Basics of Functional Programming and Scala
7.1 Basics of Functional Programming and Scala
7.2 Introduction to Scala
7.3 Demo: Scala Installation
7.3 Functional Programming
7.4 Programming with Scala
Demo: Basic Literals and Arithmetic Operators
Demo: Logical Operators
7.5 Type Inference Classes Objects and Functions in Scala
Demo: Type Inference Functions Anonymous Function and Class
7.7 Types of Collections
Demo: Five Types of Collections
Demo: Operations on List
7.8 Scala REPL
Demo: Features of Scala REPL
7.9 Key Takeaways
Basics of Functional Programming and Scala
Lesson 9 Apache Spark Next Generation Big Data Framework
8.1 Apache Spark Next Generation Big Data Framework
8.2 History of Spark
8.3 Limitations of MapReduce in Hadoop
8.4 Introduction to Apache Spark
8.5 Components of Spark
8.6 Application of In-Memory Processing
8.7 Hadoop Ecosystem vs Spark
8.8 Advantages of Spark
8.9 Spark Architecture
8.10 Spark Cluster in Real World
8.11 Demo: Running a Scala Programs in Spark Shell
8.12 Demo: Setting Up Execution Environment in IDE
8.13 Demo: Spark Web UI
8.11 Key Takeaways
Apache Spark Next Generation Big Data Framework
Lesson 10 Spark Core Processing RDD
9.1 Processing RDD
9.1 Introduction to Spark RDD
9.2 RDD in Spark
9.3 Creating Spark RDD
9.4 Pair RDD
9.5 RDD Operations
9.6 Demo: Spark Transformation Detailed Exploration Using Scala Examples
9.7 Demo: Spark Action Detailed Exploration Using Scala
9.8 Caching and Persistence
9.9 Storage Levels
9.10 Lineage and DAG
9.11 Need for DAG
9.12 Debugging in Spark
9.13 Partitioning in Spark
9.14 Scheduling in Spark
9.15 Shuffling in Spark
9.16 Sort Shuffle
9.17 Aggregating Data with Pair RDD
9.18 Demo: Spark Application with Data Written Back to HDFS and Spark UI
9.19 Demo: Changing Spark Application Parameters
9.20 Demo: Handling Different File Formats
9.21 Demo: Spark RDD with Real-World Application
9.22 Demo: Optimizing Spark Jobs
9.23 Key Takeaways
Spark Core Processing RDD
Lesson 11 Spark SQL - Processing DataFrames
10.1 Spark SQL Processing DataFrames
10.2 Spark SQL Introduction
10.3 Spark SQL Architecture
10.5 Demo: Handling Various Data Formats
10.6 Demo: Implement Various DataFrame Operations
10.7 Demo: UDF and UDAF
10.8 Interoperating with RDDs
10.9 Demo: Process DataFrame Using SQL Query
10.10 RDD vs DataFrame vs Dataset
10.11 Key Takeaways
Spark SQL - Processing DataFrames
Lesson 12 Spark MLLib - Modelling BigData with Spark
11.1 Spark MLlib Modeling Big Data with Spark
11.2 Role of Data Scientist and Data Analyst in Big Data
11.3 Analytics in Spark
11.4 Machine Learning
11.5 Supervised Learning
11.6 Demo: Classification of Linear SVM
11.7 Demo: Linear Regression with Real World Case Studies
11.8 Unsupervised Learning
11.9 Demo: Unsupervised Clustering K-Means
11.10 Reinforcement Learning
11.11 Semi-Supervised Learning
11.12 Overview of MLlib
11.13 MLlib Pipelines
11.14 Key Takeaways
Spark MLLib - Modeling BigData with Spark
Lesson 13 Stream Processing Frameworks and Spark Streaming
12.1 Stream Processing Frameworks and Spark Streaming
12.1 Streaming Overview
12.2 Real-Time Processing of Big Data
12.3 Data Processing Architectures
12.4 Demo: Real-Time Data Processing
12.5 Spark Streaming
12.6 Demo: Writing Spark Streaming Application
12.7 Introduction to DStreams
12.8 Transformations on DStreams
12.9 Design Patterns for Using ForeachRDD
12.10 State Operations
12.11 Windowing Operations
12.12 Join Operations stream-dataset Join
12.13 Demo: Windowing of Real-Time Data Processing
12.14 Streaming Sources
12.15 Demo: Processing Twitter Streaming Data
12.16 Structured Spark Streaming
12.17 Use Case Banking Transactions
12.18 Structured Streaming Architecture Model and Its Components
12.19 Output Sinks
12.20 Structured Streaming APIs
12.21 Constructing Columns in Structured Streaming
12.22 Windowed Operations on Event-Time
12.23 Use Cases
12.24 Demo: Streaming Pipeline
12.25 Key Takeaways
Stream Processing Frameworks and Spark Streaming
Lesson 14 Spark GraphX
13.1 Spark GraphX
13.2 Introduction to Graph
13.3 Graphx in Spark
13.4 Graph Operators
13.5 Join Operators
13.6 Graph Parallel System
13.7 Algorithms in Spark
13.8 Pregel API
13.9 Use Case of GraphX
13.10 Demo: GraphX Vertex Predicate
13.11 Demo: Page Rank Algorithm
13.12 Key Takeaways
13.14 Project Assistance
Car Insurance Analysis
Transactional Data Analysis
Learn Big Data and Hadoop Spark with World class Experts
Definitely a top-notch quality AWS training program in Chennai, Since the trainer here is Professional level AWS Certified solution architect he guided me well in getting my certification. The AWS WhatsApp group which they created helped me to clear the queries at any time. I would definitely suggest Credo Systemz as AWS authorized training partner in Chennai to get your amazon aws certification.
Hi everyone, I am Beula from Madurai. Joined in Credo Systemz AWS Training in Velachery in May month. As I already have work experience as a support engineer I joined here to get my AWS certification and as planned I have now completed my AWS Associate level certification examination. The trainer and the training here helped me a lot to clear my certification easily. Also, the AWS training cost in Chennai is also feasible and worth paying. You can definitely learn all the important services here practically.
Myself Sherlin joined Credo Systemz to be a certified AWS administrator for my career growth, AWS course here went practically with all the needed aws services for examination. After the session I have applied for AWS SysOps administrator examination and cleared it easily because of my trainer’s guidance during the training.
As network engineer I choose AWS certification for my future career, Joined Credo Systemz AWS training center in Velachery. The trainer here handled the session with the real time scenarios from his own experience. Assisted me whenever I need and also gave tips to clear the exam easily. AWS solution architect is my first certification exam and I have completed it successfully. Thank you Credo Systemz and my trainer.
Successfully completed my AWS solution architect certification after completing my aws course in Credo Systemz. Thank You
I will say Credo Systemz is a good place for your AWS Certification in Chennai. I recently completed my AWS training in Chennai. My trainer is a well experienced person, helped me through out the training with his own experiences without any second thought. I am very much thankful to my trainer and Credo Systemz for providing this best AWS training in Chennai.
Hai I am Jagathish, I Completed Amazon Web Services training in Credo Systemz. Joined here after had a free demo session with the trainer. He explained the concepts very clearly, He taught all AWS training topics with real time examples. Minimum AWS course fees in chennai when comparing with other institutes though providing the best aws certification course. The course content starts from basics and then moves to advanced concepts which is helpful to learn the concepts clearly.
This is Vikash.. Overall Amazon web service course in Credo Systemz was very nice, Completed all the topic thoroughly. My Trainer explained all the topics very clearly with real time practical examples which made the AWS training session very interactive. I'm sure now that I can deal with any type of AWS projects. Thank you Credo for give best AWS training..
Hai guys .. This is Vishnu and I Completed my Amazon Web Services Training in chennai at Credo Systemz. The course content was really excellent and updated topics which useful to get more knowledge about AWS. My trainer was very knowledgeable and cleared doubts clearly. All training session are very interactive and give more examples for every topics. I highly recommended Credo Systemz for doing AWS training in chennai..
I am Rajesh and did my Amazon Web Services training @ Credo Systemz. I am completely satisfied with the course. My trainer is very professional and explained all the topics very clearly with examples. This course is a very good mixture of theoretical and practical sessions. In this institute AWS training fees are really applicable to all. If my friends ask, I will surely refer my friends to take AWS Course @ Credo Systemz. Thank you Credo Systemz
To know more details about the big data Course and its services, Real-time projects and placements, Ring us ✆ +91-7358655420
We understand that once you have completed our industry standard certification programs, you will be eager to begin your career in this high growth potential industry. We go beyond training to provide useful and active lifelong assistance in various areas that will allow you to approach your career with confidence. As you work to obtain certification in our courses and join as a professional big data analyst, our team offers some important benefits. We call this our form of accelerated learning opportunities and advantages to ensure you get the initial advantage in this industry. Obviously, to take advantage of our positioning assistance, it is mandatory to delete the certification process.
First of all, Big data Solution Architect Associate level certification is one of the most popular certification for an individual who perform solution architect role. Also, this certification also has more number of job opportunities and good payscale as well, so that we have shared the certification details Such as Exam Duration, Exam level, Big data training and certification cost given below.
We are also providing assistance to clear all the levels of Big data certification exams on successfully completing the Big data course in our institute. To know more about the training and certification please feel free to reach us via ✆ +91-7358655420
We are providing the complete placement assistance for each and every candidate according to their need. Our placement approach will be unique and professional which is handled down by a separate team of experienced professionals. The team will guide you in,
Creation of resumes: In today’s highly competitive labor market, we realize that first impressions count, and a well-written and industry-centered curriculum is one of the best ways to make a lasting first impression. You will receive expert guidance and professional contributions to help you create a relevant, professional and impressive curriculum. This would be the first step to get the coveted job and we are here to help you.
Simulated interview: While a well-written resume and a social media profile help you accept the early stages, breaking the interview with senior corporate executives can be a challenge. This is where we take action, with our experience and ownership in the industry, to help you feel safe in this situation. Our mock interview sessions are designed to make you feel comfortable simulating a live interview scenario. The goal is to help you understand your weakest areas and your strengths, to help you work on the aspects that may pose a challenge in the actual interview. We are sure that by attending these sessions you can face the real interview with more confidence and be well prepared too.
Best Big Data Training Center in Chennai, Best Hadoop Training Center in Chennai, Best Big Data Training in Chennai, Best Chennai Training Institute for Big Data, Big Data Analysis Training Center in Chennai, Training of architects training big data in Chennai, certification cost of big data Chennai, training architects Hadoop in Chennai, best corporate big data training for Chennai, big data classroom training in Chennai, big date trial training in Chennai, big data certification training and positioning in Hadoop in Chennai, training in the era of big data cloud in Chennai, big data training map in Chennai, big data training Horton in Chennai, big data training of Hadoop in Chennai, Big data training institutes of Hadoop in Chennai, big data testing training in Chennai, training and location of big data in Chennai, big data company c Chennai training session, big data Corporate Hadoop Chennai training, big data workshop for students in Chennai, big data training rates in Chennai, free big data traffic entrance in Chennai, big data training Microsoft HDInsight in Chennai Velachery, Apache spark training, cloud era certification training, big data training data science, science of data using python, Chennai statistical training, Chennai spark binate training, Hadoop spark cloudier certification training, Hortonworks developer and administrator training, blue lake big data training, blue Hadoop cloudier installation, installation of Hadoop Horton works in blue, Hadoop map installation in blue, map Hadoop installation in AWS, Big Data Talen training in Chennai, solar Cassandra training in Chennai, big data training NoSQL in Velachery, the best machine learning training in big data and m Chennai, the best Big Data Deep Learning training in Chennai, the best Big Data online training in Chennai, Certification training and installation of map clusters in Chennai, Informatics big data training in Chennai, Hadoop spark NoSQL cloud training in Chennai, Python programming training in Scala Spark in Chennai, Tensor flow training in Chennai, training in spark, Hadoop work, work-oriented training big data.
A recent Tech Cocktail article discusses how Twiddly & Company Realtors reduces their costs by 15%. The company compared the maintenance charges of the contractor with the average of its other suppliers. Through this process, the company identified and eliminated invoice processing errors and automated service schedules.
The use of digital technology tools increases the efficiency of your business. Using tools such as Google Maps, Google Earth and social networks, you can perform many tasks directly on your desktop, without travel expenses. These tools also save a lot of time.
Use a business intelligence tool to evaluate your finances, which can give you a clearer idea of where your business is.
Using the same tools that large companies do allows you to be in the same field of play. Your business becomes more sophisticated by taking advantage of the tools available for use.
Small businesses must focus on the local environment they serve. Big Data allows you to further expand the likes and dislikes of your local customer. When your company knows the preferences of its customers combined with a personal touch, it has an advantage over its competition.
The fingerprints we leave behind reveal a great deal of information about our preferences, beliefs, etc. This information allows companies to adapt their products and services exactly to what the customer wants. A fingerprint is left when your customers browse online and publish on social media channels.
Recruitment firms can check candidate resumes and LinkedIn profiles for keywords that match the job description. The hiring process is no longer based on the candidate’s appearance on paper and how they are perceived personally.
How companies can analyze big data
To analyze big data, you must first identify the problems that need solutions or answers. Then try to identify the answer to your question and ask yourself: ‘How can I get the data to solve it?’ Or ‘what can big data do for my business?’
Your Big Data solutions should be easy to use, match what you thought and flexible enough to serve your business now and in the future.
Research the most reliable tool for the problem you need to solve. For example, if you want to launch more effective promotions and marketing campaigns, you can use Canopy Labs, which predicts customer behavior and sales trends.
There are many tools out there that are cheap
The two main components of HDFS are-
• NameNode – This is the master node for processing metadata information for data blocks within the HDFS
• DataNode/Slave node – This is the node which acts as slave node to store the data, for processing and use by the NameNode
In addition to serving the client requests, the NameNode executes either of two following roles –
• CheckpointNode – It runs on a different host from the NameNode
• BackupNode- It is a read-only NameNode which contains file system metadata information excluding the block locations
The two main components of YARN are–
• ResourceManager– This component receives processing requests and accordingly allocates to respective NodeManagers depending on processing needs.
• NodeManager– It executes tasks on each single Data Node
Since data analysis has become one of the key parameters of business, hence, enterprises are dealing with massive amount of structured, unstructured and semi-structured data.
Analyzing unstructured knowledge is sort of troublesome wherever Hadoop takes major give up its capabilities of
• Data collection
Moreover, Hadoop is open source and runs on commodity hardware. Hence it is a cost-benefit solution for businesses.
fsc stands for File System Check. It is a command used by HDFS. This command is used to check inconsistencies and if there is any problem in the file. For example, if there are any missing blocks for a file, HDFS gets notified through this command.
the most variations between NAS (Network-attached storage) and HDFS –
• HDFS runs on a cluster of machines while NAS runs on an individual machine.
Hence, knowledge redundancy may be a common issue in HDFS.
On the contrary, the replication protocol is totally different just in case of NAS.
Thus the chances of data redundancy are much less.
• Data is stored as data blocks in local drives in case of HDFS.
In case of NAS, it’s hold on in dedicated hardware.