Big Data & Database
Big Data Analytics Training
Turn very large datasets into decisions: big data analytics with SQL, Spark and visualisation in 16 hours
- Duration
- 16 hours
- Format
- Live online
- Modules
- 8 modules
- Certificate
- On completion
- ✓16 hours of live online instructor-led training in scheduled batches
- ✓Big data architecture concepts: data lake, warehouse and lakehouse
- ✓Analyse large data with Hive and Spark SQL
- ✓Intro to PySpark DataFrames for cleaning and aggregation
- ✓Visualise results in a BI tool and communicate insights
- ✓Case studies from retail, banking and telecom data
About the course
What this course covers
Big data analytics is the discipline of getting answers from data too large or too fast for a single database. It combines architecture knowledge (where data lives and how it is processed), query skills (SQL on distributed engines), programming (often Python), and the judgement to turn numbers into a recommendation.
This course is a practical introduction aimed at analysts and engineers. You will start with how big data platforms are built, then query large tables in distributed SQL engines, clean and transform data with PySpark DataFrames, and look at descriptive and exploratory analysis at scale. You will also see how machine learning fits in at an overview level, and how to present results through dashboards and short reports.
It is a skills course, not a vendor exam course. If you later want a particular certification, such as the Databricks Spark developer exam or a cloud data analytics credential, the concepts here give you a base.
Who should attend
- ● Data and business analysts who want to work with large datasets
- ● Reporting and MIS professionals moving into data analytics
- ● Software developers or testers interested in data careers
- ● Managers who need to understand big data projects
- ● Graduates exploring data analytics roles
Prerequisites
- ● Working knowledge of SQL, or willingness to learn it quickly
Key skills
Skills you will walk away with
Big data architecture
Describe data lakes, warehouses, lakehouses, batch and streaming layers and how they connect.
Distributed SQL
Write analytical queries with joins, aggregations and window functions on large partitioned tables.
Data cleaning at scale
Handle missing values, duplicates, type issues and skew using Spark DataFrames.
Exploratory analysis
Profile large datasets, find patterns and test hypotheses using aggregations and sampling.
File formats and partitioning
Choose Parquet or ORC and partition strategies that make queries cheaper.
Analytics with ML overview
Understand where clustering, classification and forecasting fit and how results are evaluated.
Curriculum
Module-by-module syllabus
8 modules · 32 topics · 16 hours
1Module 1: Big Data Landscape4 topics ▾
Learning objective: Understand platforms and where analytics fits.
- ▸ Volume, velocity, variety and veracity
- ▸ Data lake, warehouse and lakehouse patterns
- ▸ Batch vs streaming analytics
- ▸ Roles in a data team
2Module 2: Storage and File Formats4 topics ▾
Learning objective: Store data so it can be analysed cheaply.
- ▸ Row vs columnar formats, Parquet and ORC
- Partitioning and bucketing
Schedules
Upcoming live batches
Enroll now and choose your batch dates
Send an enquiry and we'll reserve your seat in the next batch.
Certification
How you get certified
- 1Enrol in the 16-hour live online batch
- 2Complete the SQL and PySpark lab exercises
- 3Work on the capstone analysis using the provided dataset
- 4Present your dashboard and insight summary
- 5Receive an Assure Success course completion certificate
Career outcomes
Roles this opens up
Data Analyst (Big Data)
₹4–10 LPA (approx.)Queries large datasets and builds reports and dashboards for business teams.
Business Intelligence Analyst
₹5–12 LPA (approx.)Maintains data models and BI dashboards on top of data lakes and warehouses.
Junior Data Engineer
₹5–12 LPA (approx.)Builds simple pipelines that prepare data for analysis.
Salary ranges are approximate figures for India and vary by city, company and experience.
Why this certification is worth it
Fast overview of the field
Covers architecture, querying, processing and communication in one short course.
Skills for large data
Learn techniques that still work when a spreadsheet or single database gives up.
Stepping stone
Prepares you for deeper courses such as PySpark, Snowflake or Kafka.
Why Assure Success
Learn it once, pass it once
Trainers from the field
Every session is led by a practitioner who uses this material at work.
Batches that fit your week
Weekday and weekend batches in IST, scheduled for working professionals.
Exam-style practice
Practice questions and, where available, a full simulator free with the course.
Exam voucher help
Discounted vouchers and step-by-step help booking your exam.
Human support
WhatsApp support Mon–Sat, 9 AM–8 PM IST — a person answers.
Pay your way
UPI, cards, netbanking and EMI in India; international cards accepted.
FAQs
Questions learners ask us
Q.Is this a data science course?
No. It focuses on analysing and preparing large datasets. Machine learning is covered at an overview level only.
Q.Do I need to know Python?
Not in advance. We introduce the PySpark you need, though prior Python makes the sessions smoother.
Q.Is there an exam?
This course has no external certification exam. It is a practical, project-based course.
Q.Will I use Hadoop or Spark?
You will use Spark SQL and PySpark on a prepared lab environment. Hive and Hadoop concepts are explained where relevant.
Q.Which BI tool is used?
The principles apply to any tool. Your batch will confirm the tool used for the dashboard exercise.
Q.Can I join without a technical background?
If you are comfortable with spreadsheets and willing to learn SQL basics, yes. A short SQL refresher is provided.
Q.How is this different from the PySpark course?
This one is analytics-focused and broad. The PySpark course goes much deeper into Spark programming, performance and MLlib.
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₹24,499
Big Data Analytics Training