In today’s data-driven world, organizations are increasingly relying on cloud-based solutions to manage and analyze their data effectively. GCP offers a robust ecosystem of data tools and services that empower businesses to store, process, and analyze large-scale datasets efficiently. By learning GCP data engineering, you gain the skills and knowledge to design, develop, and manage data pipelines, implement data processing systems, and build data solutions on the cloud. This expertise is in high demand, making GCP data engineering a rewarding career path.
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GCP Data Engineer Training in
Hyderabad
Google Cloud Platform (GCP) is a comprehensive cloud computing platform that offers a wide range of cloud-based services and solutions. It provides organizations with the tools and infrastructure needed to build, deploy, and scale applications efficiently. GCP enables businesses to leverage the power of Google’s infrastructure and technologies for enhanced performance and flexibility.
12 Modules
with Certifications
Certificate
After Completion
English
Language
Why Learn GCP Data Engineer in Hyderabad?
Version IT is a leading GCP Data Engineer Training Institute in Hyderabad known for its comprehensive and industry-focused training programs. When it comes to GCP data engineer training, Version IT stands out for several reasons:
Experienced Trainers: Our trainers are industry experts with extensive experience in GCP data engineering. They provide practical insights and real-world examples to ensure a well-rounded learning experience.
Hands-on Approach: We believe in learning by doing. Our GCP data engineer training program offers hands-on exercises, projects, and case studies to help you apply the concepts you learn in a practical setting.
Comprehensive Curriculum: Our training curriculum is designed to cover all the essential concepts and skills required to become a proficient GCP data engineer. From data ingestion and storage to data processing and analysis, our program covers the entire data engineering lifecycle.
Learning Objectives in GCP Data Engineer Training
During our GCP data engineer training program, you will acquire a range of skills and knowledge, including:
- Understanding the core concepts of GCP and its data-related services
- Designing and implementing data storage solutions on GCP
- Building and managing data pipelines using GCP tools like Dataflow and Dataproc
- Applying data processing techniques with technologies like BigQuery and Cloud Pub/Sub
- Integrating machine learning models and analytics solutions in GCP
- Implementing security and compliance measures for data on GCP
- Monitoring, troubleshooting, and optimizing data workflows on GCP
Who is Eligible to Learn GCP Data Engineer Training?
GCP data engineer training is suitable for professionals who are interested in working with data and want to leverage the power of the Google Cloud Platform. The following individuals can benefit from this training:
- Data engineers and ETL developers looking to expand their skills to the cloud
- Database administrators and data architects interested in cloud-based data solutions
- Data analysts and scientists who want to learn data engineering on GCP
- Software engineers and developers transitioning into the data engineering domain
- IT professionals aiming to enhance their career prospects in the cloud computing industry
To make the most of our GCP data engineer training program, participants should have a basic understanding of data concepts, SQL, and programming languages like Python. Familiarity with cloud computing concepts and prior experience with any cloud platform would be beneficial but not mandatory.
Join Version IT’s Best GCP Training in Hyderabad to gain in-demand skills and accelerate your career in the exciting field of cloud-based data engineering.
Join Version IT’s Best GCP Training in Hyderabad to gain in-demand skills and accelerate your career in the exciting field of cloud-based data engineering.
Topics You will Learn
Designing data processing systems
Designing flexible data representations. Considerations include:
- future advances in data technology
- changes to business requirements
- awareness of current state and how to migrate the design to a future state
- data modeling
- tradeoffs
- distributed systems
- schema design
Designing data pipelines. Considerations include:
- future advances in data technology
- changes to business requirements
- awareness of current state and how to migrate the design to a future state
- data modeling
- tradeoffs
- system availability
- distributed systems
- schema design
- common sources of error (eg. removing selection bias
Designing data processing infrastructure. Considerations include:
- future advances in data technology
- changes to business requirements
- awareness of current state, how to migrate the design to the future state
- data modeling
- tradeoffs
- system availability
- distributed systems
- schema design
- capacity planning
- different types of architectures: message brokers, message queues, middleware, serviceoriented
Building and maintaining data structures and databases
Building and maintaining flexible data representations
Building and maintaining pipelines. Considerations include:
- data cleansing
- batch and streaming
- transformation
- acquire and import data
- testing and quality control
- Connecting to new data sources
Building and maintaining processing infrastructure. Considerations include:
- provisioning resources
- monitoring pipelines
- adjusting pipelines
- testing and quality control
Analyzing data and enabling machine learning
Analyzing data. Considerations include:
- data collection and labeling
- data visualization
- dimensionality reduction
- data cleaning/normalization
- defining success metrics
Machine learning. Considerations include:
- feature selection/engineering
- algorithm selection
- debugging a model
Machine learning model deployment. Considerations include:
- performance/cost optimization
- online/dynamic learning
Ensuring reliability
Performing quality control. Considerations include:
- verification
- building and running test suites
- pipeline monitoring
Assessing, troubleshooting, and improving data representations and data processing
infrastructure.
Recovering data. Considerations include:
- planning (e.g. fault-tolerance)
- executing (e.g., rerunning failed jobs, performing retrospective re-analysis)
- stress testing data recovery plans and processes
Data Structures or Collections
- Applications of Data structures
- Types of Collections
- Sequence
- Strings, List, Tuple, range
- Non sequence
- Set, Frozen set, Dictionary
- Strings
- What is string
- Representation of Strings
- Processing elements using indexing
- Processing elements using Iterators
- Manipulation of String using Indexing and Slicing
- String operators
- Methods of String object
- String Formatting
- String functions
- String Immutability
- Case studies
Tuple Collection
- What is tuple?
- Different ways of creating Tuple
- Method of Tuple object
- Tuple is Immutable
- Mutable and Immutable elements of Tuple
- Process tuple through Indexing and Slicing
- List v/s Tuple
Visualizing data and advocating policy
- Building (or selecting) data visualization and reporting tools. Considerations include:
automation - decision support
- data summarization, (e.g, translation up the chain, fidelity, trackability, integrity)
Advocating policies and publishing data and reports.
Designing for security and compliance
Designing secure data infrastructure and processes. Considerations include:
- Identity and Access Management (IAM)
- data security
- penetration testing
- Separation of Duties (SoD)
- security control
- Designing for legal compliance. Considerations include:
legislation (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children’s
Online Privacy Protection Act (COPPA), etc.) - audits
Operators
- Arithmetic Operators
- Comparison Operators
- Python Assignment Operators
- Logical Operators
- Bitwise Operators
- Shift operators
- Membership Operators
- Identity Operators
- Ternary Operator
- Operator precedence
- Difference between “is” vs “==”
Control Statements
- Conditional control statements
- If
- If-else
- If-elif-else
- Nested-if
- Loop control statements
- for
- while
- Nested loops
- Branching statements
- Break
- Continue
- Pass
- Return
- Case studies
List Collection
- What is List
- Need of List collection
- Different ways of creating List
- List comprehension
- List indices
- Processing elements of List through Indexing and Slicing
- List object methods
- List is Mutable
- Mutable and Immutable elements of List
- Nested Lists
- List_of_lists
- Hardcopy, shallowCopy and DeepCopy
- zip() in Python
- How to unzip?
- Python Arrays:
- Case studies
Set Collection
- What is set?
- Different ways of creating set
- Difference between list and set
- Iteration Over Sets
- Accessing elements of set
- Python Set Methods
- Python Set Operations
- Union of sets
- functions and methods of set
- Python Frozen set
- Difference between set and frozenset ?
- Case study
Let Your Certificates Speak
- Comprehensive training in GCP Data Engineer.
- Certificates are globally recognized & they upgrade your programming profile.
- Certificates are generated after the completion of course.
All You Need to Start this Course
- Basic understanding of data concepts, SQL, and programming languages like Python.
- Familiarity with cloud computing concepts and prior experience with any cloud platform would be beneficial but not mandatory.
Testimonials
I had the pleasure of attending version IT for devops course training in Ameerpet, Hyderabad.The phani sir training is highly skilled and have a great passion in terms of understanding. They make each student feel very confident and provide a great learning environment. The course material is very comprehensive and well-structured, making is to follow.
Recently I joined Azure (virtual) personally,I suggest... if anyone wants to learn Azure, you should join the Version IT Institute. because their teaching is phenomenal. Even though I am an online student, I have a great experience. The way of explanation is very understandable, providing daily notes through pdf/docx format and faculty is available 24*7 to clarify doubts. I am really glad that I joined Version IT, after joining your Institute... My skills have improved. Thank you so much for your guidance.
Gained Good knowledge and experience in Version IT. I joined here for a Salesforce CRM course and was attending online classes. My trainer was excellent the telling the way he taught was really amazing. No matter how difficult a concept is he narrates in such a way that you can understand easily. But need to have some interest first.
The training was highly informative and well and real world insights and practical tools, making it a valuable learning experience. Version IT is the best institute for GCP Devops Training in Hyderabad.
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Still Having Doubts?
A GCP Data Engineer is in charge of developing, constructing, and managing data processing systems on the Google Cloud Platform. Data storage, transformation, and analysis are examples of such jobs.
BigQuery (data warehouse), Cloud Storage (object storage), Dataflow (stream and batch processing), Dataprep (data preparation), and other data engineering services are available through GCP.
BigQuery is a serverless data warehouse that allows for quick SQL searches on massive datasets. BigQuery is used by Data Engineers for analytics, reporting, and ad-hoc querying.
Cloud Storage is Google Cloud Platform's object storage service. Cloud Storage is used by data engineers for scalable and long-term storage of raw and processed data, frequently as a data lake.
