Cloud Analytics India 2026

Setup (5 minutes)

1. Go to console.cloud.google.com — sign in with your Google account
2. Click "Select a project" → New Project → name it (e.g. evika-analytics)
3. In the left menu search bar, type "BigQuery" → click it to open the console
4. In Explorer panel → + ADD → Public datasets to access free Google datasets
5. Or click the query editor at top and start writing SQL immediately

-- Query Google's free public datasets
-- No setup needed — runs in the BigQuery console

-- Example 1: Wikipedia page views for Indian topics
SELECT
  title,
  SUM(views)    AS total_views,
  COUNT(*)      AS days_with_data
FROM `bigquery-public-data.wikipedia.table_date_range`
WHERE
  title LIKE '%India%'
  AND datehour BETWEEN TIMESTAMP('2025-01-01')
                   AND TIMESTAMP('2025-12-31')
GROUP BY title
ORDER BY total_views DESC
LIMIT 20;

-- Example 2: Use PARTITION BY for running totals (window function)
SELECT
  title,
  datehour,
  views,
  SUM(views) OVER (
    PARTITION BY title
    ORDER BY datehour
    ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
  ) AS cumulative_views
FROM `bigquery-public-data.wikipedia.table_date_range`
WHERE title = 'Taj_Mahal'
ORDER BY datehour;

-- Step 1: Upload a CSV file
-- BigQuery console → your dataset → Create table
-- Source: Upload | File format: CSV
-- Auto-detect schema: ON → Create table

-- Step 2: Query your uploaded data
SELECT
  category,
  COUNT(*)              AS transactions,
  ROUND(SUM(amount),2)  AS total_amount,
  ROUND(AVG(amount),2)  AS avg_amount
FROM `your-project.your_dataset.your_table`
WHERE DATE(transaction_date) >= DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY)
GROUP BY category
ORDER BY total_amount DESC;

-- Step 3: Export results to Google Sheets (one click)
-- In results panel: Save results → Google Sheets
-- Share the sheet with your manager — no Power BI needed

-- Create external table pointing to S3 CSV files
CREATE EXTERNAL TABLE sales_data (
  order_id      STRING,
  customer_id   STRING,
  order_date    DATE,
  amount        DOUBLE,
  city          STRING,
  status        STRING
)
ROW FORMAT DELIMITED
  FIELDS TERMINATED BY ','
STORED AS TEXTFILE
LOCATION 's3://your-bucket/sales/'
TBLPROPERTIES ('skip.header.line.count'='1');

-- Query runs against S3 directly — no data movement
SELECT
  city,
  COUNT(order_id)     AS orders,
  SUM(amount)         AS revenue
FROM sales_data
WHERE status = 'completed'
  AND YEAR(order_date) = 2026
GROUP BY city
ORDER BY revenue DESC;

• Create a free GCP account
• Query 3 different public datasets (use LIMIT 1000 to avoid scans)
• Upload your own CSV project data and query it
• Export results to Google Sheets and build a chart
• Write one complex query using window functions in BigQuery

• Connect Power BI Desktop to BigQuery via the native connector
• Build one report with BigQuery as the data source
• Publish to Power BI Service and set up scheduled refresh
• If targeting IT services: create a free Azure account and explore Synapse Studio
• Practice T-SQL in Azure Synapse (similar to SQL Server)

• Create an AWS free-tier account
• Upload a CSV to S3 and create an Athena external table
• Run 5 analytical queries in Athena
• Explore AWS QuickSight (basic BI tool, has a free tier)
• Add "BigQuery, Athena basics" to your resume skills section

Which cloud platform should a data analyst in India learn first — AWS, Azure, or GCP?

For most data analysts in India in 2026: start with Google Cloud Platform (GCP) specifically BigQuery — it has the most generous free tier (1 TB of queries per month free), the most approachable SQL interface for analysts, and widespread use at Indian startups and product companies. If your target companies are IT services or large enterprises (Infosys, TCS, HCL, Wipro client projects), learn Azure first because Microsoft dominates that segment through existing Office 365 relationships. AWS is the cloud market leader globally and pays the highest salary premium, but its analytics stack (Athena, Redshift, Glue) has a steeper learning curve for analysts. Recommended order: GCP BigQuery → Azure Synapse basics → AWS Athena.

Is cloud knowledge required for data analyst jobs in Noida and Delhi NCR?

Cloud knowledge is increasingly expected but not yet required for all data analyst roles in Delhi NCR in 2026. At IT services companies in Noida Sector 62 and similar clusters, traditional on-premise tools still dominate — Excel, SQL Server, and Power BI on local infrastructure. At startups on the Noida Expressway and Gurugram product companies, cloud data skills (especially BigQuery or Redshift) are becoming a standard expectation at the mid-senior level. For freshers and junior analysts, focus on SQL, Excel, and Power BI first — then add one cloud platform within 12-18 months of your first job. Listing "GCP BigQuery" or "Azure Data Factory basics" on your resume does differentiate you even if you have only self-taught exposure.

What is AWS Athena and how is it used for data analysis?

AWS Athena is a serverless query service that lets you run SQL queries directly against files stored in Amazon S3 — CSV, JSON, Parquet, or ORC files — without needing to load data into a traditional database. You only pay per query (around $5 per TB of data scanned). Data analysts use Athena to query large log files, event data, and exported datasets that live in S3 buckets. It uses standard SQL (Presto/Trino engine) so your existing SQL skills transfer directly. Setup: store your CSV file in an S3 bucket → create an external table in Athena pointing to that S3 path → run SQL queries. No server management required.

What is Azure Synapse Analytics and how is it different from Power BI?

Azure Synapse Analytics is Microsoft's cloud data warehouse and analytics platform — it stores and processes large datasets using SQL or Spark. Power BI is the visualisation and reporting layer that sits on top of Synapse (or any other data source) to build dashboards. They work together: Synapse handles the heavy data processing and storage; Power BI connects to Synapse to display the results. For data analysts in India, Power BI is the more immediately useful skill. Azure Synapse becomes relevant when you join a company with large datasets (hundreds of millions of rows) that a local SQL database cannot handle efficiently.

Are there free cloud certifications for data analysts in India?

Google Cloud offers free learning paths on cloud.google.com/training — the "Preparing for your Associate Cloud Engineer Examination" and "From Data to Insights with Google Cloud" skill badges are free. Microsoft offers free learning paths on learn.microsoft.com for Azure data fundamentals (DP-900) — the learning content is free but the exam itself costs approximately ₹4,500. AWS offers free learning on skillbuilder.aws — the Cloud Practitioner course is free, and the exam costs approximately ₹9,000. For data analysts, the Google Cloud "Data Analyst" learning path on Coursera (paid, but auditable for free) is the most directly relevant to day-to-day analyst work.

How do I connect Power BI to cloud data sources like BigQuery or Azure Synapse?

To connect Power BI to BigQuery: open Power BI Desktop → Get Data → Google BigQuery → authenticate with your Google account → select your project and dataset → load tables. To connect to Azure Synapse: Get Data → Azure → Azure Synapse Analytics → enter your server name (from the Synapse workspace) → authenticate → select tables. Both connections support DirectQuery mode (queries run live against the cloud source) or Import mode (data copied into Power BI). For large datasets, DirectQuery is better as data stays in the cloud; for smaller datasets or complex DAX calculations, Import mode is faster. Scheduled refresh in Power BI Service works for both — no on-premises gateway needed for cloud sources.

What cloud skills do data analyst job descriptions in India ask for in 2026?

Common cloud mentions in Indian data analyst job descriptions in 2026: BigQuery (most common at startups and product companies), Azure Data Factory and Azure Synapse (IT services and BFSI), AWS S3 and Athena (e-commerce and logistics companies), Snowflake (growing rapidly, especially at D2C and SaaS companies — not AWS/Azure/GCP but cloud-based), Databricks (advanced analytics and data engineering roles). For a fresher or junior analyst, listing basic BigQuery experience (even from personal projects) is enough to check the cloud box on most job descriptions. Deeper cloud certifications become relevant when targeting senior or data engineering adjacent roles.