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How to Kick Start Your Career in Data Engineering?
One of the most in-demand talents on the job market right now is the ability to be a data engineer. The skills and expertise needed to gather, process, store, analyze, and preserve data that may be utilized in decision-making are sought after by employers. Experts that can efficiently manage and analyze these massive volumes of data are essential as businesses become more dependent on data for their operations.
The IBM Data Engineering Professional Certificate Program is one of the finest methods to acquire the skills and information required to succeed as a data engineer. With restricted access to self-paced courses that cover important subjects like Big Data Analysis & Storage, Advanced Analytics & Artificial Intelligence, Automation & Modeling, and Machine Learning & Predictive Modeling, this program offers an intensive learning experience. Moreover, it incorporates practical laboratories and project evaluations that let students practice their abilities in actual work settings. With the official IBM recognition provided by the certificate, students may stand out from the crowd and get employment right away after graduation.
Moreover, the IBM Data Engineering program offers an exclusive career package that includes mentoring from seasoned data engineers and networking with business executives through frequent webinars hosted by IBM staff. Holders of certificates will gain more knowledge about the field of big data engineering as well as vital advice for their future professional growth from these chances. Also, during the course, e-Newsletters are given to students, enabling them to keep current on industry trends and breakthroughs. This enables graduates to stay competitive throughout recruiting procedures and to highlight recent developments in their resumes or online portfolios.
No prior knowledge of data engineering or programming is necessary for this certificate. Anybody who wishes to build job-ready skills, tools, and a portfolio for entry-level data engineer employment should consider pursuing this professional certificate. You will engage yourself in the position of a data engineer during the self-paced online courses and learn the fundamental skills required to work with a variety of tools and databases to develop, implement, and manage structured and unstructured data.
This course is free to audit for everyone. You can opt to enroll in this course with a USD 49 monthly subscription and, upon successful completion, get both the Coursera course certificate and an IBM digital badge.
You’ll be able to describe and carry out the main duties involved in a data engineering position by the time you’ve finished this Professional Certificate. To extract, transform, and load (ETL) data, you will utilize Linux/UNIX shell scripts and Airflow.
You will use Relational Database Management Systems (RDBMS) and SQL commands to query data. For unstructured data, NoSQL databases will be used.
You will also learn about big data and get to work with Hadoop and Spark, two big data engines. You’ll get practice building Data Warehouses and using business intelligence tools for analysis and insight extraction.
The IBM Data Engineering Professional Certificate is developed to provide students with the abilities and know-how necessary to succeed as data engineers. To create enterprise-level data applications and solutions, the initiative makes use of both IBM Watson’s proprietary analytics platform and open-source tools like Apache Spark and Hadoop. This certificate program offers classes on big data principles, Python programming, IBM DB2 SQL databases, cloud computing, machine learning, ETL/ELT pipelines, Apache Kafka streaming data processing, and more.
After completing this program, you’ll be equipped with the knowledge and talents required for a job in data engineering, including those for handling sizable datasets, building and implementing distributed data pipelines using cloud computing services, developing real-time streaming analytics capabilities, and designing reliable and secure programs that use massive datasets, and performing sophisticated analysis on the produced datasets.
There are 13 Courses in this Professional Certificate.
Introduction to Data Engineering
You will be introduced to the fundamental ideas, procedures, and tools you need to understand to gain a basic understanding of data engineering in this course. You will acquire knowledge of the modern data ecosystem and the functions that data engineers, data scientists, and data analysts do within it. You can learn more about the difference between these three roles in the link below:
The Data Engineering Ecosystem is made up of a variety of parts. It contains many data sources, formats, and data types. Data pipelines collect information from many sources, turn it into data that is suitable for analysis, and then make it accessible to data consumers for analysis and decision-making. These data are processed and stored in data repositories such as relational and non-relational databases, data warehouses, data marts, data lakes, and most recently lakehouses. For the benefit of the data consumers, data integration platforms aggregate several types of data into a single perspective.
Data Engineering Lifecycle
Building data platforms, creating data stores, and collecting, importing, wrangling, querying, and analyzing data are all part of a normal data engineering lifecycle. It also includes tuning and performance monitoring to make sure systems are operating at their best. You will study the data engineering lifecycle in this course. Additionally, security, governance, and compliance will be covered.
For more details about the data engineering lifecycle, refer to chapter 2 The Data Engineering Lifecycle of the book written by Joe Reis, and Matt Housley called Fundamentals of Data Engineering.
The course also includes hands-on labs that guide you through creating your IBM Cloud Lite account, provisioning a database instance, load data into the database instance, and performing some basic querying operations that help you understand your dataset.
Python for Data Science, AI & Development
Python’s flexibility and usability make it a crucial language for data engineering. It has gained widespread acceptance for a range of applications, from machine learning and deep learning to web development, and is the preferred language for many data scientists.
Data engineers with experience in PHP & MySQL discover that using Python boosts productivity. You can swiftly handle massive volumes of data or develop complicated algorithms with cutting-edge ideas like sentiment analysis or natural language processing with just a few lines of code.
Python’s library support also makes it suited for managing all types of data without substantial additional programming or investment in software or hardware, allowing you to concentrate on what matters most: your big data strategy.
By learning Python, you’ll also have access to a wealth of resources, including deep learning frameworks, pandas, and numpy, which are strong tools for creating, manipulating, and analyzing complex datasets.
Python Project for Data Engineering
By using various methods to gather and deal with data, this mini-course aims to put basic Python abilities to use. Play the part of a data engineer and gather information from various file kinds, convert it into particular datatypes, and then feed it into a single source for analysis. With the aid of several hands-on labs, continue the course and put your skills to the test by implementing web scraping and extracting data with APIs.
After finishing this course, you’ll have the self-assurance to start using Python to web-scrape websites to gather massive datasets from several sources and combine them into a single primary source.
Introduction to Relational Databases (RDBMS)
Relational databases are a valuable tool for data engineers to comprehend the structure of the data and utilize the storage system accurately and effectively. Relational databases make it possible to store data in a more ordered manner and give data scientists easy access to and control over enormous amounts of data. They support database transactions, construct triggers that can react to changes in the database state, define triggers that can act on complicated relationships between tables, and guarantee consistency in the database state.
In addition, they scale well with growing workloads and are quite affordable. A good backup strategy, security measures, and auditing use cases can all be aided by a data engineer’s thorough understanding of relational databases.
Databases and SQL for Data Science with Python
Working with databases and SQL is a crucial aspect of the function of data engineering, which is a crucial part of data science. Data engineers can efficiently store, maintain, and extract data so that it may be further processed for analysis by data scientists by having a working grasp of databases and SQL.
It is significantly more effective to use Python in conjunction with databases and SQL to handle big amounts of data efficiently. Data engineers may work with any sort of data source or technological stack that might be included in the project by mastering the foundations of databases.
Hands-on Introduction to Linux Commands and Shell Scripting
Data engineers can benefit from a better grasp of the Linux operating system, which is important for many jobs linked to data engineering, by learning Linux commands and shell scripting. It gives you a chance to master crucial ideas like shell scripting, rights administration, command line navigation, and other administrative activities.
Moreover, it can assist data engineers in designing and implementing automated procedures for handling massive data sets in a practical setting. When setting up distributed computing systems utilizing tools like Apache, Hadoop, or Spark, this kind of training may also be a beneficial resource.
Relational Database Administration (DBA)
Studying Relational Database Administration (DBA) provides a wide range of important skills and knowledge for managing information systems. DBAs use databases to protect, store and process vital data for businesses. Database administrators manage the design, implementation, and maintenance of company databases. They ensure that all data is secure and backed up in case of catastrophe. DBAs are responsible for optimizing the performance of the database, and they must troubleshoot issues when they arise.
By studying relational database administration, individuals will gain experience in problem-solving techniques, advanced analytics, user interface design principles, and software development tools and processes. In addition to the technical abilities needed to be a successful DBA, students may also learn useful communication skills as part of their studies. Understanding how to explain complex IT tasks to a variety of stakeholders is an invaluable skill in any organization.
ETL and Data Pipelines with Shell, Airflow, and Kafka
ETL and data pipelines with Shell, Airflow, and Kafka are crucial technologies for organizing and processing data effectively, thus, data engineers should master them. Although it is possible to manually transfer data from one source to another, the software makes the process simpler and more effective.
ETL stands for Extract, Transform, and Load. An open-source application called Airflow makes it easier to build intricate data pipelines in Python by giving users a platform to build automated processes that allow for simple-to-understand representations of progress.
Last but not least, Kafka is an open-source stream processing software system created for high throughput log aggregation, enabling us to efficiently and swiftly handle massive volumes of data in real-time. Understanding each of these systems gives data engineers the thorough understanding required for efficient extraction, transformation, and loading of large datasets, regardless of their level of complexity.
Getting Started with Data Warehousing and BI Analytics
Businesses looking to harness the potential of their data are more likely to find data engineers who are knowledgeable in data warehouses and business intelligence (BI) analytics to be beneficial. They can make sure that businesses get the most out of their data by knowing how to build a data warehouse, transform unstructured, semi-structured, and structured data into useful information, and provide it with particular BI needs to support business decisions.
They may also grasp the “big picture” of how data flows across an organization’s systems, databases, websites, and other sources, thanks to data warehousing and business intelligence analytics. Moreover, by creating reports and dashboards with data visualizations that enable decision makers to immediately comprehend
Introduction to NoSQL Databases
Because NoSQL databases can offer quicker data retrieval, scalability, and flexibility, data engineers should get familiar with them. Data engineers can quickly interact with other databases and applications and enable complicated queries by utilizing the strength of NoSQL databases. This enables them to design more dependable and efficient data pipelines and optimize big datasets. Additionally, NoSQL databases often require less upkeep than conventional SQL databases, which helps to eventually lower operating expenses.
You will get a technical hands-on understanding of NoSQL databases and Database-as-a-Service (DaaS) options through this course. NoSQL databases have grown much more relevant in the database environment with the introduction of Big Data and agile development approaches. Their primary benefit is their capacity to successfully address the scalability and flexibility difficulties brought up by contemporary applications.
You will first become familiar with the background and fundamentals of NoSQL databases, as well as their key features and advantages. You will become familiar with the four types of NoSQL databases and how they vary from one another. You will examine the design and capabilities of many different NoSQL database implementations, including MongoDB, Cassandra, and IBM Cloudant.
Following that, you will gain practical experience utilizing such NoSQL databases to carry out typical database administration operations, including building and replicating databases, loading and querying data, changing database permissions, indexing and aggregating data, and sharding (or splitting) data.
Introduction to Big Data with Spark and Hadoop
Spark and Hadoop are two technologies that data engineers should become familiar with to work with massive datasets fast and effectively. Data engineers can process, analyze, and display big datasets that are challenging or impossible to handle using conventional approaches utilizing technologies like Apache Spark and Apache Hadoop. These platforms may also quickly scale up to bigger datasets and offer quicker processing rates than their forerunners due to the distributed computing characteristics of these platforms.
Furthermore, both Spark and Hadoop provide a wealth of useful features, including support for SQL-like querying, interactive analytic tools, machine learning-based models for automatic insight creation, and interfaces with well-known programming languages like Python or Scala. Spark and Hadoop have become indispensable technologies for today’s data engineers.
Data Engineering and Machine Learning using Spark
For machine learning and data engineering applications, Spark is a well-liked data processing framework. It provides an extensive and potent collection of tools for efficient processing and analyzing of huge amounts of data. The machine learning and data engineering functions in Spark’s library offer a quick and easy approach to processing batch and stream data from various sources.
Furthermore, Spark interfaces with other well-known big-data systems, such as Hadoop, to offer distributed computing capabilities, enabling users to expand their projects with ease. Spark is ideally suited for handling huge datasets rapidly and effectively since it can manage numerous processing sources.
Additionally, it provides robust Python, R, and SQL language support to assure accuracy in data manipulation and analysis tasks. In the end, organizations may increase their potential to tap into the intelligence already available in their organizational systems while minimizing the amount of time needed to create and deploy complicated applications by mastering Data Engineering & Machine Learning using Apache Spark.
Data Engineering Capstone Project
You will put several data engineering skills and methods you have learned throughout the IBM Data Engineering Professional Certificate’s earlier courses to use in this course. You will play the part of a Junior Data Engineer who has just joined the company and be given a real-world use case that calls for a data engineering solution.
All things considered, this curriculum gives people who are interested in becoming effective data engineers a thorough introduction and foundation. This certification offers graduate students resources like networking events with industry players and continuing education updates that keep our alumni at the forefront of innovation when it comes to integrating new technologies within existing systems, in addition to priceless technical skill sets ranging from ETL pipelines to predictive modeling strategies. As one of the best qualifications currently offered, the IBM Data Engineering Professional Certificate Program gives graduates an advantage over other candidates fighting for similar roles across many sectors today while also providing lifelong value!
If you have completed IBM Data Engineering Professional Certificate, you can also get IBM Data Warehouse Engineer Professional Certificate for free. Don’t forget to take this certificate as well.
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