A great learning path for all consumer and science clients

Introduction

The major data source is broad and can be a very difficult task for anyone starting to learn great information and technology. Many data technologies are many and can be limited to determine where to start.

This is the reason I think about writing this article. This article provides you with a guided path to launch your journey to learn great information that will help you to find work in key industry factories. The biggest challenge we can face is to recognize the right role in our interests and ours.

To address this problem looga, I explained in detail the role of the data, we consider the role of various engineering and computer graduates.

I tried to answer all the questions you have or can get while you are learning great information. To help you choose away based on your interests, we have added a treemap that will help you to identify the right path.

1. How to start?

One of the first questions that people ask me when they want to start learning great data: "Do I learn about Hadoop, Computing Computing, Kafka, NoSQL or Spark?"

Well, I always have a response: "It depends on what you really want to do".

So, we are dealing with this problem in a positive way. We want to follow this step by step guide.

2. What roles are involved in the large data industry?

There are many roles in the major data industry. But, in general, it can be divided into two parts:

• Big Data

• Big Data Analysis

2.1 These sites are self-employed but different.

The major project is talking about the design, implementation, sales and maintenance (storage) of more information. Many engineers have to set up and operate to create the relevant data that is available for internal and internal applications.

Although Great Analytics is based on the idea of using the many data from systems developed by the Big Data engineers. Major Data Analysis includes analyzes of trends, structures and development of diversity, predictive and predictive systems.

Therefore, in large terms, Big Data Analysis is involved in the higher accounting data. Although large engine engineers are involved in designing and implementing systems and methods for calculating the calculation.

 3. What are your nature and location?

Now, we know the types of jobs available in the industry, let's try to tell you what kind of good for you. After that, you can analyze where you can fit the industry.

Generally, as noted in your learning experience and experience in the industry, we can sort out each of these as follows:

3.1 Basic Background

(This includes interests and does not necessarily reflect your college education).

1. Computing

2. Mathematics

3.2 Business experience

1. In the refrigerator

2. Data Professor

3. Computer engineering (working on data-related projects)

Therefore, using the above grades, you can define your background as follows:

Example 1: "I'm a computer graduate who has no experience in mathematics skills."

You are interested in science or mathematics science, but previous experience will not be counted.

Example 2: "I am a computer graduator and I work for information like data".

Your software is in science computer and you are a member of Engineer Computer (data-related projects).

Example 3: "I am a scholar who works as a researcher".

You are interested in Math and are relevant to the Knowledge of Knowledge.

Then, pass and define your definition.

(The types listed here are necessary to find the way you can study the major industry data). the Big Data is great and can be a great task for anyone starting to learn great information and technology. Many data technologies are many and can be limited to determine where to start.

4. 'profiles'

Now that you have identified 4. Spellings of profiles'

Now that you define your profile, let's follow and illustrate the examples you need to go.

4.1 Role of Information Systems Institutes

If you have good program skills and understand how to interact with the Internet, but not any interest in mathematics and statistics. In this case, you must choose the major engineering role.

4.2 Role of Big Data Roles

If you are good at the programs and have the education and interest in math and statistics, you have to choose Big Data Analytics.

 5. How can you be a great engineer?

At first, we describe what major engineers need to know and learn to consider the industrial space. First and foremost, you must first identify your needs. You can not just begin to learn the Big Data without knowing your needs. Otherwise, you would have to shoot in the dark.

To define your needs, you must know the general record of major data. So, let's find out what the real Greatness is.

5.1 Torture

The Big Data project has two major components: the terms of the data and the product requirements.

jargon for data requirements

Structure: As you know, the data can be stored in a file or folder. If the data are stored in a model of data (ie, on a peripheral), it is called a Data Cluster. If it is stored in the files and does not have anything previously mentioned, it is called non-systematic information. (Types: structured / unorganized)

Dimension: size, we value the amount of data. (Classes: S / M / L / XL / XXL / High)

Settlement performance: defines the speed of data transferable from the system. (Hours: H / M / L)

Response: Defines the speed of data that can be updated and modified. (Hours: H / M / L)

The work requirements of the language

Timeframe: Time required by a system to conduct inquiries. (Type: long / middle / short)

Time to work: Time required to implement data (types: long/ medium/short)

Verification: accuracy of data systems (Types: Duration / Estimated)

5.2 The structure and structure you need to know

Step 1: Create a system to analyze the sales performance of a company that creates data that comes from a wide range of sources, including customer data, data tracks, call data centres, sales data, product information, blogs, etc.

5.3 Learn how to design solutions and technologies

The solution for Scenario 1: Lake Data for Sales Data

(This is a personalized solution, you can come up with more confidential solutions if you do, lower dividing).

How, how does the engine solve the problem?

One point to remember is that the large data system is not only designed to interfere with the data obtained from various sources to do all the time but also to be designed to allowing data analysis and data usage to develop applications that are easy, fast and accessible (a rational board in this case).

Definition of the ultimate goal:

1. Make the implementation of different sources of information.

2. Direct information data from regular times (perhaps a week in this case)

3. Finding the data analysis (all day, maybe every day)

4. Building to facilitate the access and implementation of the review process.

Now we know what our ultimate goal is, we try to set our conditions unofficial.

5.3.1 Requirements related to data

Structure: Most data is designed and has clearer guidelines. But resources such as weblogs, customer interaction/data centre calling, sales data book, advertising productivity data. Finding and advertising the advertisement information and media depends on one company.

Conclusion: Structured and non-systematic data

Size: L or XL (Hadoop choice)

Flow power: high

Quality: Medium (Hadoop and Cafeteria)

Complete: Not Completed

5.3.2 Work-related conditions

Consultation time: medium to long-term

Working time: Short-lived

Confirmation: Actually

Because many resources are integrated, it is important to keep in mind that different information is accessed through a velocity system. For example, weblogs will be accessed on a regular basis with a high level.

Based on our initial analysis of our system requirements, we can recommend the following main structure.

6. Greater Education Path

Now you understand the great data industry, different roles and requirements for a great professional. Let's see the path you need to follow to become more engineers.

As we know, the large data source is full of technology. Therefore, it is important to learn the appropriate technology and follow your great workplace. This is different from any typical type, such as a science and machine engine, where you start with something and try to accomplish everything in the field.

Next time you will find the tree to pass to get a very special way. Although some of the pearl technology aims to become the strength of the data professionals, it's always good to know all the technology to the edges of the leaves if they start the road. The tree is taken from the lambda paradigm.

7. Resources

1.Bash Scripting

• Antarctic Beginning Startup Garrels Machtelt

2.Python

• Python's Catera specialist

• Learn Python's Science Information for Coursera

3. Java

• Introduction to Java 1: Begin to apply for Udemy Java

• Udemy's mediation and mediation program

• Introduce the Java 2 program in Udemy

• Java diagram of the program diagram: data and specialist systems outside Coursera

4.Kadib

• Big Data Technology Technology Amazon Web Services

• AWS's Big Data for Amazon Web Services

5. HDFS

• Big Data and Hadoop Essentials by Udemy

• Importance of Data for Higher Data in the University

• Udemy Hadoop Kit Previous

• Hadoop Apache documents

• Book: implementing Adobe unit

6. Zookeeper Apache

• Apache Zookeeper documents

• Book - Zookeeper

7. Apache Kafka

• Complete Apache Kafka course for beginner Udemy

• Provide basic apache Kafka and the highest theme of Udemy

• Apache Kafka documents

• Book - Curriculum Reading

8. SQL

• Comprehensive Data MySQL by Coursera

• SQLCourse by SQLcourse.com

• PostgreSQL Directory Guide for Udemy

• The high performance of MySQL

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