Remove Cloud Storage Remove Raw Data Remove Unstructured Data
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Setting up Data Lake on GCP using Cloud Storage and BigQuery

Analytics Vidhya

Introduction A data lake is a centralized and scalable repository storing structured and unstructured data. The need for a data lake arises from the growing volume, variety, and velocity of data companies need to manage and analyze.

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Data Warehouse vs Data Lake vs Data Lakehouse: Definitions, Similarities, and Differences

Monte Carlo

With pre-built functionalities and robust SQL support, data warehouses are tailor-made to enable swift, actionable querying for data analytics teams working primarily with structured data. Storage can utilize S3, Google Cloud Storage, Microsoft Azure Blob Storage, or Hadoop HDFS. Or maybe both.)

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Data Lake vs. Data Warehouse: Differences and Similarities

U-Next

Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. The data lakes store data from a wide variety of sources, including IoT devices, real-time social media streams, user data, and web application transactions.

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Top Data Lake Vendors (Quick Reference Guide)

Monte Carlo

By accommodating various data types, reducing preprocessing overhead, and offering scalability, data lakes have become an essential component of modern data platforms , particularly those serving streaming or machine learning use cases. Google Cloud Platform and/or BigLake Google offers a couple options for building data lakes.

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ETL vs. ELT and the Evolution of Data Integration Techniques

Ascend.io

Low in Visibility End-users won’t be able to access all the data in the final destination, only the data that was transformed and loaded. First, every transformation performed on the data pushes you further from the raw data and obscures some of the underlying information. This causes two issues.

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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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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. Data can be loaded in batches or can be streamed in near real-time. They need to be transformed.