How to become a Big Data Hadoop Architect - Learning Paths Explored
What does a Big Data Hadoop Architect do?
Typically, a Big Data Hadoop architect addresses specific Big Data problems and requirements. If you take up this role, you will be expected to describe the structure and behavior of a Big Data solution utilizing the Hadoop technology.
You will need to cater to the needs of both the organization as well as Big Data specialists and engineers, and act as a link between them. Any organization that wants to build a Big Data environment will require a Big Data architect who can manage the complete lifecycle of a Hadoop solution – including requirement analysis, platform selection, design of technical architecture, design of application design and development, testing, and deployment of the proposed solution.
Sound interesting? Here's what you need to do to get there!
Ensure you meet these primary requirements
To be a Big Data Hadoop architect, you’ve got to have advanced data mining and data analytical skills. Which requires years of professional experience in the Big Data field. If you have the skills listed here, you’re on the right track:
- Marketing and analytical skills: The ability to process and analyze data to understand the behavior of the buyer/customer.
- RDMSs (Relational Database Management Systems) or Foundational database skills
- The ability to implement and use NoSQL, Cloud Computing, and MapReduce
- Skills in statistics and applied math
- Data visualization and data migration
Moreover, your role as a data architect, will be of more importance as many businesses are now turning to data architects than a data analyst or a database engineer. A data architects with the skills to integrate data from different sources is the need of the hour. As a data architect, you will play a important role working closely with users, system designers, and developers.
What's all this fuss about Hadoop, anyway?
Datamation has this to say about Hadoop: “When it comes to tools for working with Big Data, open source solutions in general and Apache Hadoop in particular dominate the landscape. Forrester Analyst Mike Gualtieri recently predicted that "100 percent of large companies" would adopt Hadoop over the next couple of years.
Report from Market Research forecasts that the Hadoop market will grow at a compound annual growth rate (CAGR) of 58 percent through 2022 and that it will be worth more than $1 billion by 2020. IBM too believes so strongly in open source Big Data tools that it assigned 3,500 researchers to work on Apache Spark, a tool that is part of the Hadoop ecosystem.
Apache’s Hadoop has become synonymous with Big Data because its ecosystem includes various open source tools that help in “highly scalable and distributed computing”.
How do I get there?
In a field as technical and ultra-competitive as Big Data and Hadoop, acquiring an accredited, globally-recognized professional certification may be the best way to not only learn the ins and outs of the domain, but to also back it up with authoritative validation.
Our Big Data Hadoop Architect Masters Program, gives you all the knowledge and the skills that will be required to speed up your career as a Big Data Architect expert. The program has been designed keeping in mind the high-in-demand requirements of Big Data Architects in the field. This program provides access to 200+ hours of high- quality eLearning,On-demand support by hadoop experts, simulation exams, a community moderated by experts, and a Master's certificate on completition of the training.
The infographic above has laid out a series of learning paths to guide you in your journey!
What the various certifications mean
#1 Big Data and Hadoop Developer
The best way to begin is by taking up a Big Data and Hadoop Developer certification course. This course is aimed at enabling professionals to take up assignments in Big Data. Beyond covering the concepts of Hadoop 2.7, the course provides hands-on training in Big Data and Hadoop and involves candidates in projects that require the implementation of Big Data and Hadoop concepts.
Once you finish this course, you will have a thorough knowledge of MapReduce, HDFS, Pig, Hive, Hbase, Zookeeper, Flume, and Sqoop.
Software Developers and Architects, Analytics Professionals, Data Management Professionals, Business Intelligence Professionals, Project Managers, Aspiring Data Scientists, and anyone with a keen interest in Big Data Analytics – including graduates – can benefit hugely from this course.
#2 Apache Spark and Scala
What next? Apache Spark and Scala. This is aimed at equipping aspirants with skills involved in real-time processing of Hadoop.
Apache Spark is an open source cluster computing framework that helps in data “transformation” and “mapping” concepts. This framework works well with Scala or “Scalable Language”, which is a preferred workhorse language for server systems that are mission-critical in nature.
Once you’re done with this course, you can choose either of the two NoSQL databases – MongoDB or Cassandra.
- MongoDB: MongoDB is a cross-platform document-oriented database that helps in data modelling, ingestion, query and sharing, data replication and more. It is the most popular NoSQL database in the industry.
A certification course in MongoDB should be able to build your expertise in writing Java and Node JS applications using MongoDB; improve your skills in Replication and Sharding of data so you can optimize read / write performance; teach you installation, configuration, and maintenance of a MongoDB environment; and develop your proficiency in MongoDB configuration, backup methods, as well as monitoring and operational strategies.
It will also give you experience in creating and managing different types of indexes in MongoDB for query execution, and offer you a deeper understanding of managing DB Notes, replica set, and Master-Slave concepts.
To sum it up, you will be able to process huge amounts of data using MongoDB tools, and proficiently store unstructured data in MongoDB.
- Cassandra: Apache Cassandra is an open-source distributed database management system that works on the “master-and-slave” mechanism. Cassandra is best while working on write-heavy applications.
Cassandra offers greater scalability and is thus able to store petabytes of data. It is carefully designed to handle huge workloads across multiple datacenters, without a single point of failure.
A certification course in Apache Cassandra should include details on the fundamentals of Big Data and NoSQL databases; Cassandra and its features; the architecture and data model of Cassandra; installation, configuration, and monitoring of Cassandra; and Hadoop ecosystem of products around Cassandra.
#3 Apache Storm
Apache Storm is for real-time event processing with Big Data. To implement Apache Storm effectively, you need to master the fundamental concepts of Apache Storm as well as its architecture. An understanding of plan installation and configuration with Apache Storm is also necessary.
This course will give you a good understanding on ingesting and processing of real-time events with Storm, and the fundamentals of Trident extension to Apache Storm. Knowledge of grouping and data insertion in Apache Storm is essential. Plus, an understanding the fundamentals of Storm interfaces with Kafka, Cassandra, and Java.
#4 Apache Kafka
Apache Kafka is an open source Apache project, whose highlight is that it’s a high-performance real-time messaging system that can process millions of messages per second. It provides a distributed and partitioned messaging system and is highly fault-tolerant.
Before you begin, you’ve got to have a good grasp of Kafka architecture, installation, interfaces, and configuration.
With more companies round the world adapting to Kafka, it has become the preferred messaging platform for processing Big Data in real-time.
With this certification, you will be a master at handling huge amounts of data.
This is the last in the line of certifications that will lead you to becoming a Big Data Hadoop architect. Knowledge of Impala – ‘an Open Source SQL Engine for Hadoop’ – will equip you with an understanding of the basic concepts of Massively Parallel Processing (MPP), the SQL query engine that runs on Apache Hadoop. With this certification, you will be able to interpret the role of Impala in the Big Data Ecosystem.
The advantageous edge Impala would provide is the ability to query data in Apache Hadoop and skip the time-consuming steps of loading and recognizing data. Plus, you will be able to gain knowledge of Data base, SQL, data warehouse and other data base programming languages.
With that, you will be able to reach your destination. You will have a fantastic understanding of the overall IT landscape and the multitude of technologies, and above all, you will be able to analyze how different technologies work together.
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