Data Cleansing with Data Ingestion
Data cleansing is, naturally, a necessity for high-quality data. Without clean data, business intelligence and analytics efforts are hampered and overall operational efficiency is hamstrung. Data cleansing - also known as data cleaning or data scrubbing - fixes, or if necessary, removes common data errors, including missing values and typos. In a recent study, the Harvard Business Review found that only 3% of businesses surveyed hit the benchmark of 97% data record accuracy or greater.
Data Cleansing: How and When
During the data ingestion and analysis cycle, data cleansing has traditionally come earlier in the process, usually before the ETL (extract, transform, load) process, when data is at rest.
At that point, data cleansing tools scour and audit data using predefined constraints to correct errors that can potentially corrupt or render data sets useless for valuable analysis."Dirty" data that violates the constraints is placed into a separate workflow exception data handling.
Data Cleansing and the Cloud Data Warehouse
Data warehousing and data analytics require clean data. With Snowflake, the Data Warehouse Built for the Cloud, users can take advantage of tools such as Spark to build clean, highly scaleable data ingestion pipelines.
It offers a wide variety of easily-available connectors to diverse data sources and facilitates data extraction, often the first step in a complex ETL pipeline. Spark also helps with computationally-involved data transformation tasks, such as sessionization, data cleansing, data consolidation, and data unification. With the Snowflake Connector, the data in these complex ETL pipelines can be effortlessly stored in Snowflake for organization-wide self-service using SQL.
Test Drive the Data Warehouse Built for the Cloud
Spin up a Snowflake free trial to see first-hand how cloud data warehousing can help you better ingest clean data and solve data streaming issues and: