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Complete Guide to Data Transformation: Basics to Advanced

Ascend.io

What is Data Transformation? Data transformation is the process of converting raw data into a usable format to generate insights. It involves cleaning, normalizing, validating, and enriching data, ensuring that it is consistent and ready for analysis.

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Consulting Case Study: Job Market Analysis

WeCloudData

Furthermore, one cannot combine and aggregate data from publicly available job boards into custom graphs or dashboards. The client needed to build its own internal data pipeline with enough flexibility to meet the business requirements for a job market analysis platform & dashboard.

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Consulting Case Study: Job Market Analysis

WeCloudData

Furthermore, one cannot combine and aggregate data from publicly available job boards into custom graphs or dashboards. The client needed to build its own internal data pipeline with enough flexibility to meet the business requirements for a job market analysis platform & dashboard.

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Tips to Build a Robust Data Lake Infrastructure

DareData

If you work at a relatively large company, you've seen this cycle happening many times: Analytics team wants to use unstructured data on their models or analysis. For example, an industrial analytics team wants to use the logs from raw data. The Data Warehouse(s) facilitates data ingestion and enables easy access for end-users.

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How Rockset Enables SQL-Based Rollups for Streaming Data

Rockset

All data will be indexed in real-time , and Rockset’s distributed SQL engine will leverage the indexes and provide sub-second query response times. But until this release, all these data sources involved indexing the incoming raw data on a record by record basis. That is sufficient for some use cases.

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AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

ProjectPro

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses.

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Data Warehousing Guide: Fundamentals & Key Concepts

Monte Carlo

Cleaning Bad data can derail an entire company, and the foundation of bad data is unclean data. Therefore it’s of immense importance that the data that enters a data warehouse needs to be cleaned. Yes, data warehouses can store unstructured data as a blob datatype. They need to be transformed.