🌐 Data Engineering Tools & Their Use Cases 🛠️📊

🔹 Apache Kafka ➜ Real-time data streaming and event processing for high-throughput pipelines
🔹 Apache Spark ➜ Distributed data processing for batch and streaming analytics at scale
🔹 Apache Airflow ➜ Workflow orchestration and scheduling for complex ETL dependencies
🔹 dbt (Data Build Tool) ➜ SQL-based data transformation and modeling in warehouses
🔹 Snowflake ➜ Cloud data warehousing with separation of storage and compute
🔹 Apache Flink ➜ Stateful stream processing for low-latency real-time applications
🔹 Estuary Flow ➜ Unified streaming ETL for sub-100ms data integration
🔹 Databricks ➜ Lakehouse platform for collaborative data engineering and ML
🔹 Prefect ➜ Modern workflow orchestration with error handling and observability
🔹 Great Expectations ➜ Data validation and quality testing in pipelines
🔹 Delta Lake ➜ ACID transactions and versioning for reliable data lakes
🔹 Apache NiFi ➜ Data flow automation for ingestion and routing
🔹 Kubernetes ➜ Container orchestration for scalable DE infrastructure
🔹 Terraform ➜ Infrastructure as code for provisioning DE environments
🔹 MLflow ➜ Experiment tracking and model deployment in engineering workflows

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You don't need to learn Python more than this for a Data Engineering role

➊ List Comprehensions and Dict Comprehensions
↳ Optimize iteration with one-liners
↳ Fast filtering and transformations
↳ O(n) time complexity

➋ Lambda Functions
↳ Anonymous functions for concise operations
↳ Used in map(), filter(), and sort()
↳ Key for functional programming

➌ Functional Programming (map, filter, reduce)
↳ Apply transformations efficiently
↳ Reduce dataset size dynamically
↳ Avoid unnecessary loops

➍ Iterators and Generators
↳ Efficient memory handling with yield
↳ Streaming large datasets
↳ Lazy evaluation for performance

➎ Error Handling with Try-Except
↳ Graceful failure handling
↳ Preventing crashes in pipelines
↳ Custom exception classes

➏ Regex for Data Cleaning
↳ Extract structured data from unstructured text
↳ Pattern matching for text processing
↳ Optimized with re.compile()

➐ File Handling (CSV, JSON, Parquet)
↳ Read and write structured data efficiently
↳ pandas.read_csv(), json.load(), pyarrow
↳ Handling large files in chunks

➑ Handling Missing Data
↳ .fillna(), .dropna(), .interpolate()
↳ Imputing missing values
↳ Reducing nulls for better analytics

➒ Pandas Operations
↳ DataFrame filtering and aggregations
↳ .groupby(), .pivot_table(), .merge()
↳ Handling large structured datasets

➓ SQL Queries in Python
↳ Using sqlalchemy and pandas.read_sql()
↳ Writing optimized queries
↳ Connecting to databases

⓫ Working with APIs
↳ Fetching data with requests and httpx
↳ Handling rate limits and retries
↳ Parsing JSON/XML responses

⓬ Cloud Data Handling (AWS S3, Google Cloud, Azure)
↳ Upload/download data from cloud storage
↳ boto3, gcsfs, azure-storage
↳ Handling large-scale data ingestion