How to become a big data engineer

Is becoming a big data engineer right for me?

The first step to choosing a career is to make sure you are actually willing to commit to pursuing the career. You don’t want to waste your time doing something you don’t want to do. If you’re new here, you should read about:

Overview
What do big data engineers do?

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How to become a Big Data Engineer

Becoming a big data engineer requires a combination of education, skills development, and practical experience. Here's a guide on how to pursue a career in big data engineering:

  • Educational Background: Start by obtaining a bachelor's degree in a relevant field such as computer science, information technology, data science, or a related discipline. Some universities and colleges offer specialized programs or courses in big data analytics or data engineering.
  • Learn Programming Languages: Develop proficiency in programming languages commonly used in big data engineering, such as Python, Java, Scala, or SQL. Familiarize yourself with data manipulation, scripting, and querying techniques to work with large datasets efficiently.
  • Understand Data Technologies: Learn about big data technologies and frameworks such as Apache Hadoop, Apache Spark, Apache Flink, and distributed computing principles. Explore cloud platforms like AWS, Azure, or Google Cloud, and understand how to leverage cloud-based big data services and managed infrastructure.
  • Gain Experience with Data Tools: Familiarize yourself with data processing and analytics tools such as Apache Kafka, Apache NiFi, Apache Airflow, or cloud-based services like AWS Glue, Azure Data Factory, or Google Cloud Dataflow. Learn about data storage solutions like HDFS, Amazon S3, Azure Blob Storage, and databases like HBase, Cassandra, or MongoDB.
  • Master Data Processing Techniques: Learn about data processing techniques such as batch processing, stream processing, and real-time analytics. Understand concepts like data ingestion, data transformation, and data enrichment to prepare data for analysis and insights generation.
  • Practice with Projects and Exercises: Apply your knowledge and skills by working on hands-on projects, exercises, and coding challenges related to big data engineering. Build data pipelines, ETL processes, and analytics solutions using real-world datasets and scenarios to gain practical experience.
  • Obtain Certifications: Consider obtaining certifications in big data technologies and platforms to validate your skills and enhance your credibility as a big data engineer (see below).
  • Build a Professional Network: Network with professionals in the big data industry, including data engineers, data scientists, and IT professionals, through online forums, LinkedIn, professional events, and meetups. Networking can help you learn about job opportunities, gain insights into industry trends, and connect with potential mentors or collaborators.
  • Apply for Entry-Level Positions or Internships: Look for entry-level positions, internships, or apprenticeships in big data engineering roles at companies, startups, or research institutions. Gain hands-on experience working on real-world projects and learn from experienced professionals in the field.

Certifications
There are several certifications available for big data engineers, offered by various organizations and vendors. These certifications validate your skills and expertise in big data technologies and platforms.

  • Cloudera Certified Professional (CCP) Data Engineer: Offered by Cloudera, this certification validates your ability to design, develop, and manage data processing systems using Cloudera's platform, including Apache Hadoop, Apache Spark, and related technologies.
  • Hortonworks Data Platform (HDP) Certified Developer: Offered by Hortonworks, this certification demonstrates your proficiency in building and optimizing data processing applications using the Hortonworks Data Platform, which includes Apache Hadoop, Apache Spark, and other big data tools.
  • AWS Certified Big Data - Specialty: Offered by Amazon Web Services (AWS), this certification validates your expertise in designing and implementing big data solutions on the AWS platform, including services like Amazon EMR, Amazon Redshift, Amazon Kinesis, and AWS Glue.
  • Microsoft Certified: Azure Data Engineer Associate: Offered by Microsoft, this certification demonstrates your ability to design and implement data solutions using Azure data services, including Azure Databricks, Azure Data Lake Storage, Azure SQL Data Warehouse, and Azure Stream Analytics.
  • Google Cloud Professional Data Engineer: Offered by Google Cloud, this certification validates your skills in designing, building, and managing data processing systems and analytics solutions on the Google Cloud Platform (GCP), including services like BigQuery, Dataflow, Dataproc, and TensorFlow.
  • IBM Certified Data Engineer - Big Data: Offered by IBM, this certification validates your ability to design, build, and deploy data processing systems using IBM's big data technologies, including IBM BigInsights, IBM Db2 Big SQL, and IBM InfoSphere BigInsights.
  • Databricks Certified Associate Developer for Apache Spark 3.0: Offered by Databricks, this certification validates your proficiency in building data engineering pipelines and applications using Apache Spark, a popular distributed computing framework for big data processing.
  • DataStax Apache Cassandra™ 3.x Developer Associate Certification: Offered by DataStax, this certification validates your knowledge and skills in developing applications and data models using Apache Cassandra, a highly scalable and distributed NoSQL database.