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Data Profiling (Ad-hoc Data Quality Analysis):
Profile the data to understand its structure, data types, and distribution. Look for anomalies, missing values, and outliers. This helps in identifying potential data quality issues. We have helped our customers identify missing data chunks and anomalies in the data.
Data Consistency Checks
Define data validation rules that check data for compliance with expected patterns and business rules. The data validation rules cover both syntactic & semantic validation rules. For example, in syntactic validation - you can check that email addresses are in a valid format. In semantic validation -you can check that dates fall within a specified range.
Data Cleaning and Transformation
Implement data cleaning and transformation processes to correct errors, standardize data formats, and handle missing values. This can include techniques like data imputation, data masking, and data anonymization for PII data.
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Data Standardization & Data Deduplication
Standardize data to ensure consistency. For example, convert text to uppercase, normalize addresses, or align date formats. Identify and remove duplicate records to ensure that each data entry is unique. Deduplication can be based on key fields or a combination of attributes.
Data Lineage
Maintain data lineage information to track the source, transformation, and flow of data. This helps in understanding where data quality issues may have originated. With data lineage you can trace the origin of every piece of data which helps to fix the problem at the source.
Error Handling, Monitoring and Logging
Implement robust error handling and logging mechanisms to capture and report data quality issues in real-time. Set up alerts for critical errors. Error monitoring dashboards & alerting can be implemented through tools like DataDog, Splunk and PagerDuty.
Case Studies
Revolutionizing Data Infrastructure for AI-Driven Green Energy Solutions
JashDS revolutionized a green energy tech company's data infrastructure by implementing a scalable Matillion-based ETL solution and automated CI/CD processes, resulting in 2-3x faster client onboarding and a 35% reduction in Google Cloud costs. The comprehensive solution included reusable components, optimized SQL queries, and efficient data aggregation techniques, enhancing the client's ability to process vast amounts of utility data from 40+ companies and support their AI-driven green energy initiatives.
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