Data Sourcing: Collecting data from various internal (transactional databases, CRM systems) and external sources (social media, IoT devices, APIs).

Data Ingestion: Loading raw data into a storage system, such as a data lake or distributed storage cluster.

Data Streaming: Capturing real-time data streams (e.g., website interactions, sensor data) for immediate or near-real-time analysis.

Data Lake and Data Warehouse Setup: Storing raw data in data lakes (e.g., Hadoop, AWS S3) and structured data in data warehouses (e.g., Snowflake, BigQuery).

Distributed Storage Management: Using distributed file systems like Hadoop Distributed File System (HDFS) to handle large datasets.

Data Cataloging and Metadata Management: Organizing data with metadata, making it easier to search, classify, and retrieve.

Data Cleansing: Removing inaccuracies, duplicates, or inconsistencies to improve data quality.

Data Transformation: Converting raw data into a usable format (e.g., encoding, aggregating) for analysis or machine learning.

Handling Missing Values: Identifying and addressing missing data using techniques like imputation, deletion, or flagging.

ETL Pipelines: Building pipelines to extract, transform, and load data from different sources into a consolidated database or data warehouse.

Data Consolidation: Integrating data from various sources to create a unified dataset for analysis.

Data Synchronization: Keeping data consistent across systems and storage platforms, often in real-time.

Batch Processing: Processing large volumes of data at once using tools like Apache Spark and Hadoop.

Real-Time Processing: Handling streaming data to provide real-time insights, often using tools like Apache Kafka, Apache Flink, or Spark Streaming.

Data Mining: Identifying patterns, correlations, and anomalies within large datasets through statistical and machine learning techniques.

Descriptive Analytics: Summarizing historical data to understand trends, patterns, and anomalies.

Predictive Analytics: Building and training machine learning models on large datasets to predict future outcomes.

Prescriptive Analytics: Using optimization and simulation models to provide recommendations based on big data insights.

Data Visualization: Creating interactive dashboards and visualizations to present insights using tools like Tableau, Power BI, and D3.js.

Reporting Automation: Automating report generation to provide regular updates on big data metrics and KPIs.

Exploratory Data Analysis (EDA): Performing statistical analysis and visual exploration to identify trends and correlations in data.

Data Encryption and Protection: Ensuring data is stored and transferred securely, using encryption and secure data access protocols.

Access Control: Implementing role-based access to data, limiting who can view, edit, or process data.

Compliance Management: Ensuring that data storage, processing, and sharing comply with regulations like GDPR, CCPA, or HIPAA.

Cluster Management: Managing and scaling storage and compute clusters (e.g., Hadoop, Spark clusters) to handle large datasets.

Resource Allocation: Efficiently distributing computational resources to maximize processing efficiency.

Query Optimization: Optimizing queries for large datasets to improve retrieval times and reduce computational costs.

Data Lineage Tracking: Documenting where data comes from, how it has been transformed, and where it’s being used.

Data Quality Monitoring: Continuously monitoring data quality to ensure consistency, accuracy, and reliability.

Data Stewardship: Establishing roles and responsibilities for managing and maintaining data standards across the organization.

Data Documentation: Creating comprehensive documentation for datasets, data pipelines, and analyses to improve transparency and usability.

Knowledge Sharing: Facilitating collaboration between data scientists, analysts, and business users to make data insights accessible.

Training and Support: Providing training on big data tools, technologies, and best practices to ensure team competency.

Pipeline Maintenance: Ensuring data pipelines and workflows remain operational and free from issues.

Model and Pipeline Updates: Updating machine learning models and data pipelines as new data becomes available.

Continuous Improvement: Monitoring big data processes to identify bottlenecks and optimize for speed, accuracy, and efficiency.

Feedback Loops: Collecting user feedback and real-world data to refine and improve the model.

Experimentation and R&D: Experimenting with new algorithms, technologies, or techniques for future enhancements.

Feature and Model Updates: Regularly adding new features or improving existing ones based on user feedback or industry advancements.