Data Transformation
Analyzing information requires structured and accessible data for best results. Data transformation enables organizations to alter the structure and format of raw data as needed. Learn how your enterprise can transform its data to perform analytics efficiently.
Data transformation is the process of changing the format, structure, or values of data. For data analytics projects, data may be transformed at two stages of the data pipeline. Organizations that use on-premises data warehouses generally use an ETL (extract, transform, load) process, in which data transformation is the middle step. Today, most organizations use cloud-based data warehouses, which can scale compute and storage resources with latency measured in seconds or minutes. The scalability of the cloud platform lets organizations skip preload transformations and load raw data into the data warehouse, then transform it at query time — a model called ELT ( extract, load, transform).
Processes such as data integration, data migration, data warehousing, and data wrangling all may involve data transformation. Data transformation may be constructive (adding, copying, and replicating data), destructive (deleting fields and records), aesthetic (standardizing salutations or street names), or structural (renaming, moving, and combining columns in a database). An enterprise can choose among a variety of ETL tools that automate the process of data transformation. Data analysts, data engineers, and data scientists also transform data using scripting languages such as Python or domain-specific languages like SQL.
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Datoptron Services
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Data Transformation
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Linked Data Publication
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Linked Data Integration
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Platform Development
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User Engagement
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Use of Standards & Vocabularies
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