Remove ETL Tools Remove Raw Data Remove Unstructured Data
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Data Vault on Snowflake: Feature Engineering and Business Vault

Snowflake

Collecting, cleaning, and organizing data into a coherent form for business users to consume are all standard data modeling and data engineering tasks for loading a data warehouse. The transformations we apply under feature engineering prepares the data for ML model training.

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What Is Data Engineering And What Does A Data Engineer Do? 

Meltano

What Is Data Engineering? Data engineering is the process of designing systems for collecting, storing, and analyzing large volumes of data. Put simply, it is the process of making raw data usable and accessible to data scientists, business analysts, and other team members who rely on data.

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Top ETL Use Cases for BI and Analytics:Real-World Examples

ProjectPro

It is extremely important for businesses to process data correctly since the volume and complexity of raw data are rapidly growing. Over the past few years, data-driven enterprises have succeeded with the Extract Transform Load (ETL) process to promote seamless enterprise data exchange.

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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.

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Moving Past ETL and ELT: Understanding the EtLT Approach

Ascend.io

For example, unlike traditional platforms with set schemas, data lakes adapt to frequently changing data structures at points where the data is loaded , accessed, and used. These fluid conditions require unstructured data environments that natively operate with constantly changing formats, data structures, and data semantics.

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What is Data Extraction? Examples, Tools & Techniques

Knowledge Hut

In today's world, where data rules the roost, data extraction is the key to unlocking its hidden treasures. As someone deeply immersed in the world of data science, I know that raw data is the lifeblood of innovation, decision-making, and business progress. What is data extraction?

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What is Data Transformation?

Grouparoo

The difference here is that warehoused data is in its raw form, with the transformation only performed on-demand following information access. Another benefit is that this approach supports optimizing the data transforming processes all analytical processing evolves. featured image via unsplash