Data Ingestion
Collecting data from various sources such as databases, APIs, IoT devices, logs, and external systems.
Ensuring data is ingested in real-time, near real-time, or batch mode, depending on the use case.
Tools: ADF, SSIS, Python, Dataflow, DLT
Data Storage
Storing raw and processed data in scalable and cost-effective storage systems.
Using databases (SQL/NoSQL), data lakes, or data warehouses depending on the structure and volume of data.
Tools: Amazon S3, Google BigQuery, Snowflake, Azure Data Lake, PostgreSQL, MongoDB.
Data Processing
Cleaning, transforming, and enriching raw data to make it usable for analysis.
Handling structured, semi-structured, and unstructured data.
Tools: Apache Spark, Python, PySpark , Pandas, Databricks, AWS Glue.
Data Pipeline Orchestration
Automating and managing workflows to ensure data flows seamlessly from source to destination.
Scheduling, monitoring, and error handling for data pipelines.
Tools: Apache Airflow, Luigi, Prefect, AWS Step Functions.
Data Integration
Combining data from multiple sources into a unified view.
Ensuring consistency and accuracy across integrated datasets.
Data Quality and Governance
Ensuring data accuracy, completeness, and reliability.
Implementing data validation, deduplication, and error correction.
Enforcing data governance policies for security, compliance, and privacy.
Scalability and Performance Optimization
Designing systems to handle large-scale data processing and storage.
Optimizing queries, pipelines, and infrastructure for performance.
Tools: Cloud-native services (AWS, GCP, Azure).
Data Security
Protecting data from unauthorized access, breaches, and leaks.
Implementing encryption, access controls, and auditing.
Monitoring and Maintenance
Continuously monitoring data pipelines and systems for failures, latency, or bottlenecks.
Performing regular maintenance and updates to ensure smooth operations.
Collaboration with Data Science and Analytics Teams
Providing clean, structured, and well-documented datasets for analysis and machine learning.
Supporting data scientists and analysts with the infrastructure they need.