Data Extraction
Identifying Data Sources:
Databases: Extracting data from relational databases such as MySQL, Oracle, SQL Server, or NoSQL databases like MongoDB or Cassandra.
Websites: Scraping data from web pages using web scraping tools or libraries, accessing APIs provided by websites, or using browser automation techniques.
Documents: Extracting text or structured data from documents such as PDFs, Word files, spreadsheets, or emails using text extraction tools or optical character recognition (OCR) software.
Logs and Files: Parsing and extracting data from log files, system logs, XML files, JSON files, CSV files, or other types of structured or semi-structured files.
Data Extraction Techniques:
SQL Queries: Writing SQL queries to extract data from databases based on specific criteria, filters, or joins.
Web Scraping: Crawling web pages and extracting data using techniques such as HTML parsing, XPath queries, CSS selectors, or headless browsers.
API Calls: Accessing data from web APIs by sending HTTP requests and receiving JSON, XML, or other data formats in response.
Text Extraction: Parsing and extracting text or structured data from documents using text processing techniques, regular expressions, or specialized libraries.
Data Integration Tools: Using specialized data integration or ETL (Extract, Transform, Load) tools such as Apache NiFi, Talend, Informatica, or Pentaho to extract, transform, and load data from multiple sources.
Data Transformation and Cleaning:
Data Formatting: Converting data into a standardized format, such as CSV, JSON, XML, or database tables, for consistency and compatibility.
Data Cleaning: Removing duplicates, inconsistencies, errors, or irrelevant information from the extracted data to improve data quality and accuracy.
Data Enrichment: Enhancing extracted data with additional information from external sources or by performing calculations, aggregations, or data validations.
Automated Data Extraction:
Scheduled Jobs: Setting up automated scripts, jobs, or workflows to extract data at regular intervals or specific times using cron jobs, batch processing, or scheduling tools.
Trigger-based Extraction: Configuring triggers or event-driven mechanisms to initiate data extraction processes in response to changes or updates in data sources.
Robotic Process Automation (RPA): Using software robots or bots to automate repetitive tasks and extract data from user interfaces, legacy systems, or desktop applications.
Data Security and Compliance:
Data Privacy: Ensuring compliance with data privacy regulations such as GDPR, CCPA, HIPAA, or PCI DSS by implementing data masking, encryption, anonymization, or access controls during data extraction and handling.
Data Governance: Establishing policies, procedures, and controls to manage data extraction processes, monitor data usage, and enforce data security standards across the organization.