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شبکه:📈 تحلیل کانال تلگرام Data Engineers
کانال Data Engineers (@sql_engineer) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 11 039 مشترک است و جایگاه 17 791 را در دسته آموزش و رتبه 34 853 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 11 039 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 15 سپتامبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 224 و در ۲۴ ساعت گذشته برابر 8 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 10.79% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً N/A% واکنش نسبت به کل مشترکان کسب میکند.
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- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 5 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند sql, learning, analytic, engineer, link:- تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“Free Data Engineering Ebooks & Courses”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 16 سپتامبر, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کردهاند.
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پستهای کانال
Example:
Source Database → CDC → Only Changed Records → Data Platform
CDC is especially useful for keeping analytical systems synchronized with operational databases.
⚠️ 9. Challenges in Data Ingestion
A production ingestion pipeline must handle:
Duplicate Data, Missing Data, Schema Changes, Late Data, Network Failures, High Volume
🛡️ 10. Important Data Ingestion Best Practices
A reliable ingestion pipeline should include:
✅ Incremental processing
✅ Retry mechanisms
✅ Error handling
✅ Data validation
✅ Monitoring and alerting
✅ Idempotent processing
✅ Schema validation
✅ Checkpointing for streaming systems
🌍 Real-World Example
Website → Orders Database → CDC → Kafka → Spark → Data Lake → Data Warehouse → Power BI
When a customer places an order, the event can be captured, processed, stored, and eventually used by analysts for reporting.
🎯 Interview Question
❓ What is the difference between data ingestion and data transformation?
Data ingestion focuses on moving data from a source to a destination.
Data transformation focuses on changing, cleaning, enriching, or restructuring that data.
Example:
Database → Ingestion → Move the data → Transformation → Clean & modify the data → Warehouse
💡 Key Takeaway
Remember:
📥 Data Ingestion = Get the data into the platform
📦 Batch = Process periodically
⚡ Streaming = Process continuously
🔄 Incremental = Process only new/changed data
🔍 CDC = Capture source changes
A strong understanding of ingestion is essential before moving into advanced topics like Kafka, Spark, Airflow, and cloud data pipelines.
🔥 Double Tap ❤️ For More
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| 2 | 🚀 Data Engineering Fundamentals – Part 7
📥 Data Ingestion: How Data Enters a Data Platform
Data ingestion is one of the first steps in almost every data engineering pipeline.
In simple terms:
Data ingestion = collecting data from different sources and moving it into a system where it can be stored and processed.
📌 1. What is Data Ingestion?
Data ingestion is the process of collecting data from various sources and transferring it to a destination such as:
Data Lake, Data Warehouse, Database, Lakehouse, Streaming platform
Example:
CRM ────────┐
API ────────┤
Database ───┼──→ Data Ingestion → Data Lake/Warehouse
Kafka ──────┤
Files ──────┘
🔄 2. Types of Data Ingestion
There are two major types:
📦 Batch Ingestion – Data is collected and transferred in batches at specific intervals.
⚡ Real-Time Ingestion – Data is transferred continuously as it is generated.
📦 3. Batch Ingestion
Batch ingestion processes data periodically.
Example: A company collects all sales transactions during the day and loads them into the warehouse every night.
8 AM ──┐
12 PM ─┤
4 PM ──┤ → Daily Batch → Warehouse
8 PM ──┘
Common Use Cases: Daily reports, Payroll, Monthly financial processing, Historical data migration
Advantages: ✅ Simple architecture, ✅ Easier monitoring, ✅ Cost-effective
Disadvantages: ❌ Data is not immediately available, ❌ Higher latency
⚡ 4. Real-Time Ingestion
Real-time ingestion continuously captures and transfers data as events occur.
Example:
Payment → Event Generated → Kafka → Stream Processor → Analytics System
The data can become available within seconds or milliseconds, depending on the architecture.
Use Cases: Fraud detection, Real-time monitoring, Stock market systems, IoT applications, Live recommendations
📊 Batch vs Real-Time
Batch: Periodic, Higher latency, Simpler, Usually cheaper, Example: Daily reports
Real-Time: Continuous, Low latency, More complex, Can be more expensive, Example: Fraud detection
📌 5. Common Data Sources
Data Engineers may ingest data from:
🗄️ Databases: PostgreSQL, MySQL, Oracle, SQL Server
🌐 APIs: REST APIs, GraphQL APIs
📄 Files: CSV, JSON, XML, Parquet
📡 Streaming Systems: Kafka, Kinesis, Pub/Sub
☁️ Cloud Applications: CRM, ERP, SaaS applications
🛠️ 6. Common Data Ingestion Tools
Batch: Apache Airflow, AWS Glue, Fivetran, Airbyte
Streaming: Apache Kafka, Amazon Kinesis, Google Pub/Sub, Apache Flink
🔄 7. Full Load vs Incremental Load
Full Load: Transfers the entire dataset.
Source → ALL Data → Destination
Useful when: Loading a table for the first time, Dataset is relatively small, Complete refresh is required
Incremental Load: Transfers only new or changed data.
Source → New/Changed Data → Destination
Example: If a table has 100 million records but only 50,000 changed today, an incremental pipeline processes those 50,000.
✅ Faster, ✅ Lower cost, ✅ Better scalability
🔥 8. Change Data Capture (CDC)
CDC is a technique for identifying changes in a source database.
It can capture: INSERT, UPDATE, DELETE | 1 062 |
| 3 | 🚀 Data Engineering Fundamentals – Part 6
📌 ETL vs ELT: How Data Moves from Source to Destination
ETL and ELT are two of the most important concepts in Data Engineering.
Both are used to move and transform data, but the order of operations is different.
👉 ETL = Extract → Transform → Load
👉 ELT = Extract → Load → Transform
🔄 1. What is ETL?
ETL stands for: Extract → Transform → Load
Data is extracted from the source, transformed before loading, and then stored in the target system.
Example:
Source Database → Extract → Transform → Load → Data Warehouse
Transformation Examples:
Remove duplicates
Handle NULL values
Convert data types
Standardize formats
Apply business rules
Aggregate data
☁️ 2. What is ELT?
ELT stands for: Extract → Load → Transform
Raw data is first loaded into the target platform and transformed afterward.
Example:
Source Database → Extract → Load → Data Warehouse/Lake → Transform
Modern cloud platforms have made ELT increasingly popular because they provide scalable compute for transformations.
📊 ETL vs ELT
Feature: ETL vs ELT
Transformation: Before loading vs After loading
Raw data: Usually not retained in target vs Usually retained
Processing: External ETL engine vs Target platform
Scalability: More limited vs Highly scalable
Common use: Traditional systems vs Modern cloud platforms
🏦 Real-World Example
ETL Approach
Banking Systems → ETL Tool → Clean & Transform → Data Warehouse → Power BI
The data is cleaned before entering the warehouse.
ELT Approach
Banking Systems → Data Lake/Warehouse → SQL/dbt Transformations → Analytics Tables → Power BI
Raw data is retained and transformed inside the target platform.
🧠 When Should You Use ETL?
ETL can be useful when:
✅ Data needs significant transformation before storage
✅ The target system should only contain processed data
✅ Sensitive data needs to be filtered before loading
✅ Working with legacy architectures
🚀 When Should You Use ELT?
ELT is useful when:
✅ Working with modern cloud warehouses
✅ You want to retain raw data
✅ Large-scale transformations are required
✅ You need flexibility to transform data later
🛠️ Common Tools
ETL: Informatica, Talend, AWS Glue, SSIS
ELT: dbt, Fivetran, Airbyte, Snowflake, BigQuery
🎯 Interview Question
❓ Why is ELT becoming more popular than traditional ETL?
Answer:
Modern cloud data platforms provide scalable storage and compute resources. Therefore, organizations can load raw data first and perform transformations inside the warehouse or lakehouse.
This provides greater flexibility, scalability, and easier access to raw historical data.
💡 Easy Way to Remember
ETL: Transform first → Store later
ELT: Store first → Transform later
The fundamental difference is simply where and when transformation happens.
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| 5 | Data Warehouse
Stores:
Cleaned sales data
Customer KPIs
Revenue reports
Historical business data
Used for dashboards and reporting.
Data Lakehouse
Combines raw and processed data in one platform, allowing analysts and data scientists to run analytics and machine learning workloads without maintaining separate storage systems.
🎯 Which One Should You Use?
✅ Use a Database for day-to-day transactional applications.
✅ Use a Data Warehouse for reporting, dashboards, and business intelligence.
✅ Use a Data Lake for storing massive amounts of raw data from multiple sources.
✅ Use a Lakehouse when you need both scalable storage and high-performance analytics in a single platform.
💡 Key Takeaway
Every modern data platform uses one or more of these storage systems.
As a Data Engineer, you should understand:
What each system is designed for
When to use each one
Their advantages and limitations
How they work together in a modern data architecture
🚀 Double Tap ❤️ For More | 1 779 |
| 6 | 🚀 Data Engineering Fundamentals – Part 4
📌 Databases vs Data Warehouses vs Data Lakes vs Lakehouses
One of the most common interview questions for Data Engineers is understanding the difference between these four data storage systems.
Although they all store data, each serves a different purpose.
🗄️ 1. Database
A database is designed to store and manage current operational data for day-to-day business activities.
It is optimized for fast inserts, updates, and deletes.
Characteristics
✅ Stores current operational data
✅ Supports frequent transactions
✅ Highly structured
✅ Optimized for fast reads and writes
Examples
Customer information
Banking transactions
E-commerce orders
Inventory management
Popular Databases
MySQL
PostgreSQL
SQL Server
Oracle
🏢 2. Data Warehouse
A data warehouse stores cleaned, structured, and historical data collected from multiple sources.
It is optimized for reporting, analytics, and business intelligence.
Characteristics
✅ Stores historical data
✅ Optimized for analytical queries
✅ Combines data from multiple systems
✅ Supports dashboards and reporting
Examples
Sales analysis
Financial reporting
Customer behavior analysis
Executive dashboards
Popular Data Warehouses
Snowflake
Google BigQuery
Amazon Redshift
🌊 3. Data Lake
A data lake stores raw data in its original format.
It can handle structured, semi-structured, and unstructured data.
Characteristics
✅ Stores raw data
✅ Supports all data types
✅ Highly scalable
✅ Low-cost storage
Examples
JSON files
Images
Videos
IoT sensor data
Application logs
CSV files
Popular Storage Platforms
Amazon S3
Azure Data Lake Storage
Google Cloud Storage
🏗️ 4. Data Lakehouse
A data lakehouse combines the flexibility of a data lake with the performance and reliability of a data warehouse.
It allows organizations to store raw data while also supporting high-performance analytics.
Characteristics
✅ Supports structured and unstructured data
✅ ACID transactions
✅ High-performance analytics
✅ Schema enforcement
✅ Scalable and cost-effective
Popular Lakehouse Technologies
Delta Lake
Apache Iceberg
Apache Hudi
📊 Quick Comparison
Data Type:
Database: Structured
Data Warehouse: Structured
Data Lake: All Types
Lakehouse: All Types
Data Format:
Database: Processed
Data Warehouse: Processed
Data Lake: Raw
Lakehouse: Raw + Processed
Primary Use:
Database: Transactions
Data Warehouse: Analytics
Data Lake: Storage
Lakehouse: Analytics + Storage
Query Speed:
Database: Fast
Data Warehouse: Very Fast
Data Lake: Moderate
Lakehouse: Fast
Historical Data:
Database: Limited
Data Warehouse: Yes
Data Lake: Yes
Lakehouse: Yes
🌍 Real-World Example
Imagine an online shopping company:
Database
Stores:
Customer accounts
Orders
Payments
Product inventory
Used for daily business operations.
Data Lake
Stores:
Website logs
Product images
Clickstream data
API responses
Customer reviews
Used for storing raw data. | 1 600 |
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| 9 | 🚀 Top 20 Data Engineering Terms You Should Know
1. Data Engineering
Data Engineering is the practice of designing, building, and maintaining systems that collect, process, transform, and store data for analytics, reporting, and machine learning.
2. Data Pipeline
A data pipeline is an automated workflow that moves data from one or more sources to a destination while applying transformations such as cleaning, validation, and aggregation.
3. ETL (Extract, Transform, Load)
ETL is a process where data is extracted from source systems, transformed into the required format, and then loaded into a data warehouse or database.
4. ELT (Extract, Load, Transform)
ELT is a modern data integration approach where raw data is first loaded into a data warehouse and then transformed using the warehouse's computing power.
5. Data Lake
A data lake is a centralized repository that stores large volumes of raw, structured, semi-structured, and unstructured data in its original format.
6. Data Warehouse
A data warehouse is a centralized database designed to store cleaned, structured, and historical data optimized for reporting, business intelligence, and analytics.
7. Batch Processing
Batch processing is the execution of data processing tasks on a collection of data at scheduled intervals rather than processing each event as it arrives.
8. Stream Processing
Stream processing is the continuous processing of data in real time as it is generated, enabling immediate analysis and decision-making.
9. Big Data
Big Data refers to extremely large and complex datasets that cannot be efficiently processed using traditional database systems due to their volume, velocity, and variety.
10. Apache Spark
Apache Spark is an open-source distributed computing framework used for fast processing of large datasets through in-memory computation.
11. Apache Kafka
Apache Kafka is a distributed event-streaming platform used to publish, store, and process real-time data streams between applications.
12. Partitioning
Partitioning is the process of dividing large datasets into smaller, manageable parts so they can be processed efficiently and in parallel.
13. DataFrame
A DataFrame is a distributed table-like data structure in Spark that organizes data into rows and columns with a defined schema for efficient processing.
14. Schema
A schema defines the structure of a dataset or database, including tables, columns, data types, relationships, and constraints.
15. Change Data Capture (CDC)
Change Data Capture (CDC) is a technique that identifies and captures only the data that has changed since the last processing cycle, making data pipelines faster and more efficient.
16. Data Modeling
Data modeling is the process of designing how data is organized, stored, and related to support efficient querying and analysis.
17. Data Quality
Data quality refers to the accuracy, completeness, consistency, validity, and reliability of data used for business decisions.
18. Data Lineage
Data lineage tracks the journey of data from its source through transformations to its final destination, helping with debugging, auditing, and compliance.
19. Data Governance
Data governance is the framework of policies, standards, and processes that ensure data is secure, consistent, compliant, and properly managed across an organization.
20. Fault Tolerance
Fault tolerance is the ability of a system to continue operating correctly even when one or more components fail, ensuring high availability and reliability.
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| 12 | 🚀 Top Skills Every Data Engineer Should Learn 📊🔥
🧠 1. SQL Mastery
✔ Complex Queries
✔ JOINS & Window Functions
✔ Query Optimization
✔ Data Modeling
✔ Stored Procedures
🐍 2. Programming Skills
✔ Python for Automation
✔ APIs & JSON
✔ Data Processing Scripts
✔ Error Handling
🛠 Libraries to Learn:
✔ Pandas
✔ PySpark
✔ Requests
⚡ 3. ETL & Data Pipelines
✔ Extract, Transform, Load
✔ Workflow Automation
✔ Scheduling Jobs
✔ Monitoring Pipelines
🛠 Tools to Learn:
✔ Apache Airflow
✔ dbt
✔ Prefect
☁️ 4. Cloud Platforms
✔ Cloud Storage
✔ Data Lakes
✔ Scalable Processing
✔ Cloud Security Basics
🛠 Platforms to Learn:
✔ AWS
✔ Microsoft Azure
✔ Google Cloud Platform
📊 5. Big Data Technologies
✔ Distributed Computing
✔ Real-Time Streaming
✔ Batch Processing
✔ Scalable Systems
🛠 Technologies to Learn:
✔ Apache Spark
✔ Hadoop
✔ Apache Kafka
🗄 6. Databases & Warehousing
✔ Relational Databases
✔ NoSQL Databases
✔ Data Warehouses
✔ Schema Design
🛠 Databases to Learn:
✔ PostgreSQL
✔ MongoDB
✔ Snowflake
✔ BigQuery
🔄 7. DevOps & Deployment
✔ Version Control
✔ Containerization
✔ CI/CD Basics
✔ Deployment Automation
🛠 Tools to Learn:
✔ Git
✔ Docker
✔ Kubernetes
💡 Data Engineers don’t just move data… they build the backbone of modern AI & analytics systems.
💬 Tap ❤️ if this helped you! | 1 636 |
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Students | Freshers | Working Professionals | Career Switchers
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https://rebrand.ly/Business-analyst-webinar | 1 549 |
| 14 | What is the difference between data scientist, data engineer, data analyst and business intelligence?
🧑🔬 Data Scientist
Focus: Using data to build models, make predictions, and solve complex problems.
Cleans and analyzes data
Builds machine learning models
Answers “Why is this happening?” and “What will happen next?”
Works with statistics, algorithms, and coding (Python, R)
Example: Predict which customers are likely to cancel next month
🛠️ Data Engineer
Focus: Building and maintaining the systems that move and store data.
Designs and builds data pipelines (ETL/ELT)
Manages databases, data lakes, and warehouses
Ensures data is clean, reliable, and ready for others to use
Uses tools like SQL, Airflow, Spark, and cloud platforms (AWS, Azure, GCP)
Example: Create a system that collects app data every hour and stores it in a warehouse
📊 Data Analyst
Focus: Exploring data and finding insights to answer business questions.
Pulls and visualizes data (dashboards, reports)
Answers “What happened?” or “What’s going on right now?”
Works with SQL, Excel, and tools like Tableau or Power BI
Less coding and modeling than a data scientist
Example: Analyze monthly sales and show trends by region
📈 Business Intelligence (BI) Professional
Focus: Helping teams and leadership understand data through reports and dashboards.
Designs dashboards and KPIs (key performance indicators)
Translates data into stories for non-technical users
Often overlaps with data analyst role but more focused on reporting
Tools: Power BI, Looker, Tableau, Qlik
Example: Build a dashboard showing company performance by department
🧩 Summary Table
Data Scientist - What will happen? Tools: Python, R, ML tools, predictions & models
Data Engineer - How does the data move and get stored? Tools: SQL, Spark, cloud tools, infrastructure & pipelines
Data Analyst - What happened? Tools: SQL, Excel, BI tools, reports & exploration
BI Professional - How can we see business performance clearly? Tools: Power BI, Tableau, dashboards & insights for decision-makers
🎯 In short:
Data Engineers build the roads.
Data Scientists drive smart cars to predict traffic.
Data Analysts look at traffic data to see patterns.
BI Professionals show everyone the traffic report on a screen. | 1 849 |
| 15 | ✅ Skills Required to Become a Data Engineer ⚙️🚀
🧠 PROGRAMMING
1. Python (Data Pipelines)
2. Java / Scala
3. Object-Oriented Programming
4. Scripting (Automation)
5. Debugging Skills
6. Code Optimization
7. API Handling
8. Version Control (Git)
🗄️ DATABASES
1. SQL (Advanced Queries)
2. NoSQL (MongoDB, Cassandra)
3. Database Design
4. Data Modeling
5. Indexing Partitioning
6. Query Optimization
7. Data Warehousing
8. OLTP vs OLAP
⚙️ ETL / ELT
1. Data Extraction
2. Data Transformation
3. Data Loading
4. Pipeline Building
5. Workflow Automation
6. Data Integration
7. Batch Processing
8. Real-time Processing
☁️ BIG DATA TECHNOLOGIES
1. Hadoop
2. Spark
3. Kafka
4. Hive
5. Flink
6. Distributed Systems
7. Cluster Computing
8. Stream Processing
☁️ CLOUD PLATFORMS
1. AWS (S3, Redshift, Glue)
2. Azure (Data Factory, Synapse)
3. Google Cloud (BigQuery)
4. Cloud Storage
5. Serverless Architecture
6. Data Lakes
7. Security IAM
8. Cost Optimization
📊 DATA PIPELINES
1. Building Scalable Pipelines
2. Data Orchestration (Airflow)
3. Scheduling Jobs
4. Monitoring Pipelines
5. Error Handling
6. Logging Systems
7. Data Reliability
8. Performance Tuning
🧱 DATA ARCHITECTURE
1. Data Lakes
2. Data Warehouses
3. Lakehouse Architecture
4. Schema Design
5. Data Governance
6. Data Security
7. Metadata Management
8. Scalability Planning
🔍 DEVOPS TOOLS
1. Docker
2. Kubernetes
3. CI/CD Pipelines
4. Linux Basics
5. Shell Scripting
6. Git GitHub
7. Monitoring Tools
8. Infrastructure as Code
💬 Tap ❤️ if this helped you follow for more Data Engineering content! | 1 874 |
| 16 |  Every day you login... Work.. and logout.
Days become months.
Months become years.
But nothing changes.
Same role. Same work. Same pay.
Meanwhile, others are moving into Cloud & Data Engineering…
building real systems and earning better.
If you are looking to get into Azure Data Engineering then..
𝗝𝗼𝗶𝗻 𝘁𝗵𝗲 3 months 𝗟𝗶𝘃𝗲 𝗣𝗿𝗼𝗴𝗿𝗮𝗺
📌 Start Date: 20th April 2026
⏰ Time: 9 PM – 10 PM IST | Monday
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🎟️ 𝗝𝗼𝗶𝗻 𝗪𝗵𝗮𝘁𝘀𝗔𝗽𝗽 𝗚𝗿𝗼𝘂𝗽:
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🏀 𝗖𝗼𝘂𝗿𝘀𝗲 𝗖𝗼𝗻𝘁𝗲𝗻𝘁:
https://drive.google.com/file/d/1QKqhRMHx2SDNDTmPAf3_54fA6LljKHm6/view | 514 |
| 17 | 🧠 SQL Interview Question (Running Total of Sales)
📌
sales(order_id, order_date, amount)
❓ Ques :
👉 Calculate the running total of sales for each day
👉 Return order_date, daily_sales, running_total
🧩 How Interviewers Expect You to Think
• Aggregate sales per day 📊
• Use window function for cumulative sum
• Order data correctly for running calculation
💡 SQL Solution
WITH daily_sales AS (
SELECT
order_date,
SUM(amount) AS daily_sales
FROM sales
GROUP BY order_date
)
SELECT
order_date,
daily_sales,
SUM(daily_sales) OVER (
ORDER BY order_date
) AS running_total
FROM daily_sales;
🔥 Why This Question Is Powerful
• Tests window functions (must-know) 🧠
• Very common in real-world reporting
• Frequently asked in analyst & BI roles
❤️ React for more SQL interview questions 🚀 | 1 994 |
| 18 |  🔰 Python function with an example | 1 709 |
| 19 | WhatsApp is no longer a platform just for chat.
It's an educational goldmine.
If you do, you’re sleeping on a goldmine of knowledge and community. WhatsApp channels are a great way to practice data science, make your own community, and find accountability partners.
I have curated the list of best WhatsApp channels to learn coding & data science for FREE
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ENJOY LEARNING 👍👍 | 2 062 |
| 20 | 🚀 Microsoft Fabric – Most In-Demand Technology
Upgrade your skills with Microsoft Fabric and stay ahead in modern data platforms, real-time analytics, and end-to-end data solutions.
🔗 Join WhatsApp Group:
https://chat.whatsapp.com/KUtaLEliyb240g3UpdIS2U
For more information, join the group and stay updated with the latest insights.
Limited spots available – Join now. | 1 618 |
