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Data Engineering

₹30,000.00

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Data Engineering

Become a Skilled and
Job-Ready Data Engineering

  • Mentorship by industry experts
  • Job-ready portfolio of IO capstone projects
  • Ticket to fast-tracked career growth in data science
  • Opens doors to a wide range of Data Engineering job opportunities
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    What is data engineering?

    Data engineering is a field within data science and computer science that focuses on designing, building, and maintaining systems and infrastructure for collecting, storing, processing, and analyzing large volumes of data. Data engineers work closely with data scientists, analysts, and other stakeholders to ensure that data pipelines are robust, efficient, and reliable, enabling the organization to derive valuable insights from their data.
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    Key aspects of data engineering include:

    Data Collection:

    Data engineers design and implement systems to collect data from various sources, such as databases, APIs, log files, sensors, and streaming platforms. This may involve real-time data ingestion or batch processing depending on the requirements.

    Data Storage:

    Data engineers are responsible for selecting and configuring appropriate storage solutions to store the collected data efficiently and securely. This includes traditional relational databases, data warehouses, NoSQL databases, data lakes, and cloud storage services.

    Data Integration:

    Data engineers integrate data from disparate sources to create unified views of the data for analysis. This may involve data consolidation, data replication, and data synchronization across different systems and platforms.

    Data Processing:

    Data engineers develop data processing pipelines to clean, transform, and enrich raw data into a format that is suitable for analysis. This involves tasks such as data cleansing, normalization, aggregation, and feature engineering.

    Data Quality and Governance:

    Data engineers implement processes and tools to ensure data quality, integrity, and consistency throughout the data lifecycle. This includes data validation, error handling, data lineage tracking, and compliance with regulatory requirements.

    Scalability and Performance:

    Data engineers design scalable and high-performance data pipelines that can handle large volumes of data efficiently. This may involve parallel processing, distributed computing, and optimization techniques to improve throughput and reduce latency.

    Monitoring and Maintenance:

    Data engineers monitor the health and performance of data pipelines, troubleshoot issues, and perform regular maintenance to ensure that the systems are running smoothly. This includes monitoring data quality, resource utilization, and system reliability.

    Overall, data engineering plays a critical role in enabling organizations to leverage their data assets effectively for decision-making, product development, and strategic planning. By building robust data infrastructure and pipelines, data engineers empower data-driven insights and innovation within the organization. Why learn data engineering ?

    Learning data engineering offers numerous benefits, both for individuals and organizations. Here are some of the key reasons why learning data engineering is valuable:

    High Demand:

    There is a significant and growing demand for data engineers in various industries. Organizations are increasingly relying on data-driven decision-making, leading to a surge in demand for professionals who can design, build, and maintain data infrastructure and pipelines.

    Lucrative Career Opportunities:

    Data engineering skills are highly sought after, leading to attractive career opportunities and competitive salaries. Data engineers are in high demand across industries such as technology, finance, healthcare, e-commerce, and more.

    Contribution to Data-Driven Culture:

    Data engineers play a crucial role in establishing and maintaining a data-driven culture within organizations. By building robust data pipelines and infrastructure, they enable data scientists, analysts, and decision-makers to derive valuable insights from data, leading to better-informed decisions and improved business outcomes.

    Versatility and Transferable Skills:

    Data engineering skills are highly transferable across industries and domains. Whether you're working in finance, healthcare, retail, or any other sector, the fundamental principles of data engineering remain the same. This versatility opens up opportunities for professionals to explore different industries and roles throughout their careers.

    Opportunity for Innovation:

    Data engineering provides opportunities for innovation and creativity in designing and implementing data solutions. Whether it's optimizing data pipelines for performance and scalability, implementing real-time data processing, or leveraging emerging technologies such as machine learning and AI, data engineers have the opportunity to drive innovation within their organizations.

    Stay Relevant in a Data-Driven World:

    In today's data-driven world, organizations that effectively harness data have a competitive advantage. By learning data engineering, individuals can position themselves as valuable assets to organizations seeking to leverage data for strategic decision-making and business growth.

    Continuous Learning and Growth:

    Data engineering is a dynamic field that continuously evolves with advancements in technology and changing business requirements. Learning data engineering offers the opportunity for continuous learning and professional growth, as professionals stay updated on the latest tools, techniques, and best practices in the field.

    Overall, learning data engineering provides individuals with the skills and knowledge needed to succeed in a rapidly evolving and data-driven job market, while also enabling organizations to unlock the full potential of their data assets.

    Career opportunities in data engineering?

    Career opportunities in data engineering are abundant and diverse, spanning various industries and sectors. As organizations increasingly rely on data-driven decision-making and digital transformation, the demand for skilled data engineers continues to grow. Here are some common career opportunities in data engineering:

    Data Engineer:

    The primary role of a data engineer involves designing, building, and maintaining data pipelines and infrastructure. Data engineers work with large volumes of data, ensuring its availability, reliability, and accessibility for analysis and decision-making.

    Big Data Engineer:

    Big data engineers specialize in handling massive volumes of data using technologies such as Hadoop, Spark, and other distributed computing frameworks. They design and implement scalable data solutions to process, store, and analyze big data efficiently.

    Data Architect:

    Data architects are responsible for designing and implementing the overall data architecture and strategy within an organization. They define data models, schemas, and data integration patterns to ensure consistency, scalability, and usability across different data systems and applications.

    ETL (Extract, Transform, Load) Developer:

    ETL developers focus on building and maintaining ETL processes to extract data from various sources, transform it into a usable format, and load it into a data warehouse or other storage systems. They work with tools like Apache NiFi, Talend, Informatica, and AWS Glue.

    Data Warehouse Engineer:

    Data warehouse engineers specialize in designing, building, and optimizing data warehouse solutions. They work with platforms like Amazon Redshift, Google BigQuery, Snowflake, and Microsoft Azure SQL Data Warehouse to support analytics and reporting requirements.

    Streaming Data Engineer:

    Streaming data engineers design and develop real-time data processing systems to handle continuous streams of data from sources such as IoT devices, sensors, social media feeds, and application logs. They work with technologies like Apache Kafka, Apache Flink, and Apache Spark Streaming.

    Machine Learning Engineer:

    Machine learning engineers focus on building and deploying machine learning models that leverage large datasets for predictive analytics, pattern recognition, and decision-making. Data engineering skills are essential for managing data pipelines and feature engineering in machine learning workflows.

    Cloud Data Engineer:

    Cloud data engineers specialize in designing and implementing data solutions using cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). They leverage cloud-native services for storage, processing, and analytics, such as AWS S3, Azure Data Lake, and Google BigQuery.

    DataOps Engineer:

    DataOps engineers focus on automating and optimizing data operations processes, including data pipeline orchestration, monitoring, and deployment. They use DevOps principles and practices to streamline the development and deployment of data pipelines and analytics solutions.

    These are just a few examples of the diverse career opportunities available in data engineering. With the increasing importance of data in decision-making and business strategy, data engineers play a critical role in driving innovation and success across industries.

    Why Choose BrowseJobs?

    At BrowseJobs, we’re committed to more than just delivering content; we’re dedicated to providing career-oriented training programs that equip our students with the skills and knowledge they need to excel in the workforce. Our approach ensures that students emerge from our programs with the expertise and confidence of seasoned professionals, ready to make an immediate impact in their chosen field.
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    Career opportunities in data engineering? How Do We Achieve This?

    Real-World Projects:

    Our training methodology immerses students in simulated real-life projects, mirroring the challenges and dynamics of professional environments. This hands-on experience enables them to develop practical skills that are directly applicable to the workplace.

    Communication and Confidence Building:

    We prioritize the development of communication skills and confidence, recognizing their pivotal role in project success and career advancement. Through interactive exercises and coaching, students learn how to effectively articulate ideas, collaborate with colleagues, and navigate professional interactions.

    Tool Proficiency:

    We provide comprehensive training on essential tools utilized in real-world settings, including Git, GitHub, Agile methodologies, Jira, and Confluence. Mastery of these tools enhances students' productivity, collaboration, and project management capabilities.

    Problem-Solving Skills:

    Our curriculum is designed to expose students to common challenges encountered in professional settings and equip them with effective problem-solving strategies. By tackling real-world scenarios, students develop critical thinking abilities and the resilience needed to overcome obstacles.

    Team Dynamics and Collaboration:

    Understanding the dynamics of team structures and fostering effective collaboration are integral parts of our training. Students learn how to work cohesively within diverse teams, leveraging each member's strengths to achieve project objectives efficiently.

    Accessible Curriculum:

    Our programs are designed to cater to individuals with varying levels of prior knowledge and experience. Whether you're new to IT and computers or seeking to enhance your existing skill set, our curriculum is tailored to meet your needs and facilitate your learning journey.

    At BrowseJobs, we’re committed to empowering our students with the practical skills, industry insights, and professional acumen needed to thrive in today’s competitive job market. Join us and embark on a journey towards a successful and fulfilling career.

    How is this different to other data engineering courses?

    Practical Application Focus:

    Unlike traditional data engineering courses that primarily deliver theoretical knowledge, our program emphasizes practical application. By simulating real-world projects, students gain hands-on experience, allowing them to transition seamlessly into professional roles upon completion.

    Communication and Soft Skills Development:

    While technical proficiency is essential, we understand the importance of soft skills in a professional setting. Our course not only covers technical aspects but also prioritizes communication, teamwork, and problem-solving skills, ensuring that students are well-rounded and equipped to excel in any work environment.

    Tool Proficiency:

    Many data engineering courses focus solely on technical concepts, neglecting the tools and technologies used in real-world scenarios. Our program fills this gap by providing comprehensive training on industry-standard tools such as Git, GitHub, Agile methodologies, Jira, and Confluence. This ensures that students are proficient in the tools commonly utilized in professional data engineering environments.

    Problem-Solving and Critical Thinking:

    Data engineering is inherently problem-solving oriented. Our curriculum goes beyond theory to present students with real-world challenges commonly encountered in data engineering projects. By honing their problem-solving and critical thinking skills, students are better prepared to tackle complex issues and drive innovation in their future roles.

    Tailored Curriculum for All Levels:

    Whether students are beginners with no prior background in IT or seasoned professionals looking to enhance their skills, our course is designed to accommodate learners at all levels. Our accessible curriculum ensures that everyone, regardless of their starting point, can benefit from our comprehensive training program.

    Career-Readiness:

    Ultimately, our data engineering course is not just about acquiring knowledge; it's about preparing students for successful careers in the field. By providing them with the skills, confidence, and practical experience needed to thrive in professional environments, we empower our graduates to stand out in the competitive job market and make meaningful contributions from day one.

    Overall, our data engineering course distinguishes itself by offering a holistic approach that combines technical expertise with soft skills development, practical application, and career readiness, setting our students up for long-term success in the dynamic field of data engineering.

    Our Alumni Work in Top Companies

    Data Engineering Certificate

    On completion of the course, you will be encouraged and assisted to crack 10+ valuable certificates in different domains.

    Syllabus of data engineering ?

    • Python Intro
    • Comments
    • Variables
    • Data Types
    • Numbers
    • Casting
    • Strings
    • Functions
    • Lambda Functions
    • Booleans
    • Operators
    • Lists
    • Tuples
    • Sets
    • Dictionaries
    • If Else
    • While Loop | For Loops

    Python advanced:

    • Arrays
    • Classes | Objects
    • Inheritance
    • Iterators
    • Polymorphism
    • Scope
    • Modules
    • Dates
    • Math
    • Booleans
    • Operators
    • Lists
    • Tuples
    • Sets
    • Dictionaries
    • If Else
    • While Loop | For Loops

    Pandas:

    • Intro
    • Series
    • DataFrames
    • Read CSV
    • Read JSON
    • Analyse Data
    • Cleaning Data
    • Cleaning empty cells
    • Cleaning Wrong Format
    • Cleaning Wrong Data
    • Removing Duplicates
    • Pandas Correlations
    • Pandas Plotting

    SQL:

    • Intro
    • Classes | Obje⦁ Syntaxcts
    • Select
    • Select
    • Select Distinct
    • Where
    • Order by
    • And | Or | Not
    • Insert Into
    • In | Between
    • Aliases
    • Insert Into
    • Null Values
    • Update
    • Delete
    • Delete
    • Select Top
    • Min and Max
    • Count | Sum | Avg
    • Like | Wildcards

    SQL Advanced:

    • Joins
    • Left Join
    • Right Join
    • Self Join
    • Group By
    • Having
    • Exists
    • Case
    • Stored Procedures
    • Operators
    • Create DB | Table
    • Drop DB | Table
    • Alter DB | Table
    • Primary Key
    • Foriegn Key
    • Views

    Pyspark

    • Features
    • Advantages
    • Modules and Packages
    • Cluster Managers
    • Installation
    • Architecture
    • Sparksession
    • Sparkcontext
    • Sparksession
    • Sparkcontext
    • RDD
    • Parallelize
    • Repartition or Coalsce
    • Broadcast Variables
    • Accumalator

    Pyspark Dataframes cont:

    • Union() & Unionall()
    • UnionByName()
    • UDF
    • Tranform()
    • Apply()
    • Map()
    • FlatMap()
    • Foreach()
    • Sample() vs sampleBy()
    • Fillna() & fill()
    • Pivot()
    • PartitionBy()
    • MapType

    Pyspark datasources:

    • Read and Write CSV Files
    • Read and Write Parquet Files
    • Read & Write JSON Files
    • Read Hive Table
    • Save Hive Table
    • Read JDBC in parallel
    • Query Data base Table
    • Read and Write MySql
    • Read and Write Sql Server
    • Read JDBC Table

    Pyspark builtin functions:

    • When()
    • Expr()
    • lit()
    • Split()
    • Substring()
    • Translate()
    • Overlay()
    • To_timestamp()
    • To_date()
    • Datediff()
    • Explode()
    • Array()
    • Collect_llist()
    • Map_values()
    • Count_distinct()
    • Sum() | avg()
    • Rank() | Dense_rank()

    AWS concepts:

    • Intro
    • Cloud Computing
    • Benefits
    • EC2
    • EC2 Instance Types
    • EC2 Pricing
    • EC2 Scaling
    • EC2 Auto Scaling
    • AWS Load Balancing
    • AWS Lambda
    • AWS Containers
    • AWS Availability zones
    • AWS CloudFormation
    • AWS Elastic Beanstalk
    • AWS S3
    • AWS RDS
    • AWS Redshift

    GIT

    • Ntro
    • New files
    • Staging Environment
    • Commit
    • Help
    • Branch
    • Branch Merge
    • Pull From GitHub
    • Push to GitHub
    • GitHub Branch
    • Pull Branch from GitHub
    • Push Branch to Github
    • Github Flow
    • Github Fork
    • Git Clone from Github
    • Git ignore
    • Rever | Reset | Amend

    Apache Airflow:

    • Fundamental concepts
    • Working with Taskflow
    • Building a running pipeline
    • Object Storage

    Pyspark sql functions:

    • Aggregrate Functions
    • Window Functions
    • Date and Timestamp Functions
    • JSON functions

    AWS concepts:

    • IAM
    • CloudWatch
    • AWS cloud compliance

    Find Post Graduate Program in Data Engineering in other cities

    What do our students say about Data Engineering Course?

    Uttam Singh
    Uttam Singh
    ★★★★★
    Read More
    Had a fantastic experience at BrowseJobs. BrowseJobs is the goto place for anyone looking to get into the IT sector. Excellent training and placement are provided here. A fabulous team led by Krish Bhargav Sir and instructors Dhanya mam, Priyanka mam & Jeethu Sir. BrowseJobs helped me a lot with Data Engineering. Would highly recommend BrowseJobs to all those to aspire to get into tech.
    Shiva
    Shiva
    ★★★★★
    Read More
    Browsejobs Technologies Pvt Ltd - a company never requires domain knowledge or prior experience , training the students...providing them sufficient knowledge on what they're learning and giving 100 percent placement...me as a student of browsejobs ...gained very good knowledge of python from the scratch to till deployment. I think BrowseJobs is the only company which provides 100 percent placement in 90 days of training period.
    Hemanth Reddy
    Hemanth Reddy
    ★★★★★
    Read More
    BrowseJobs not only equipped me with technical expertise but also guided me through the entire placement process, helping me secure a rewarding job in the field of data engineering. Thanks to the BrowseJobs team.
    Ragavendra
    Ragavendra
    ★★★★★
    Read More
    The personalized attention and support from the trainers at BrowseJobs made the complex concepts in data engineering easy to understand, leading to a significant improvement in my technical proficiency. Overall had a great experience.
    Abhishek
    Abhishek
    ★★★★★
    Read More
    The hands-on experience provided during the data engineering course at BrowseJobs was great and helpful, giving me practical skills that I could immediately apply in real-world scenarios. The service they provide is very good and I recommend it for those who are looking for a job.
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    Some Frequently Asked Questions About Data Engineering Course Training

    What is the duration of the data engineering course, including placement assistance?

    90 Days is the total duration

    Data Engineer / Senior Data Engineer / Big Data Engineer / Data Architect

    Salary depends upon standard market value and per years of experience. The average salary might be 15- 20 LPA

    The workshop is Live and outside working hours, so your work will not be hampered. But the sessions will be recorded so you can access it any time if you not able to make it to the live sessions.

    Yes, this is a live session but even better than that!

    Yes it will be covered.

    Yes mentors are available to address your doubts or assist you.

    We have a team who will be assisting you with your resume-building and preparations at every step by analyzing your profile.

    Can you provide testimonials or success stories from previous students who have been placed?

    Sure, Please check our Website’s Home page for it.

    Yes, a Course completion certificate will be issued

    Yes, you will have lifetime access to the course you will be completing.

    Just a basic Degree / Any Graduate with basic communication skills is eligible to take our Course.

    We will have 2 or more batches starting monthly.

    Stay updated through our newsletter, website, and social media channels.

    Post-workshop support includes access to resources, community forums, and email support. If you sign up for the advanced course then you ll be given real-time projects, communication classes, confidence classes, mock interviews, and guaranteed interviews from top companies.

    You can get all the details related to the timings and dates of the workshop at the top of the page / in our Home Page.

    Discuss With A Career Advisor

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