Category: Data Science

Z-Ordering in Apache Spark

Learn how Z-Ordering in Apache Spark and Delta Lake optimizes query performance by clustering data based on specific columns. Discover how it reduces data scans, improves query speed, and enhances big data analytics efficiency.

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Schema Enforcement in Delta Lake

Schema Enforcement in Delta Lake Schema enforcement (also known as schema validation) is a key feature of Delta Lake, which ensures that the data being written to a Delta table matches a defined schema. This helps to maintain...

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Load Data from On-Premises to ADLS

Loading data from an on-premises data source to Azure Data Lake Storage (ADLS) using Azure Data Factory (ADF) involves the following steps. The key component for connecting on-premises data sources is the Self-Hosted Integration...

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Email Notifications in ADF

In Azure Data Factory (ADF), you can set up email notifications to alert users when certain events occur, such as the success or failure of a pipeline or trigger execution. Here’s how you can implement it: 1. Using Logic Apps...

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Triggers in ADF

In Azure Data Factory (ADF), triggers are used to schedule and automate pipeline execution. They allow pipelines to run on specific schedules, in response to events, or when manually invoked. Triggers help orchestrate and manage...

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Data Science and Technologies used

Here’s a table detailing key components of Data Science and the technologies commonly used: Data Science Component Description Technologies Used Data Collection Gathering raw data from various sources. SQL, MongoDB, APIs, Web...

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Delta Table in Azure Databricks

Delta Table in Azure Databricks Delta Tables in Azure Databricks leverage the Delta Lake technology to provide ACID transactions, time travel, and other advanced features for data reliability and performance. These tables can be...

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