Data Ingestion and ETL - Artificial Intelligence Zone

Most Frequently Asked Azure Data Factory Interview Questions

Analytics Vidhya

FEBRUARY 20, 2023

Introduction Azure data factory (ADF) is a cloud-based data ingestion and ETL (Extract, Transform, Load) tool. The data-driven workflow in ADF orchestrates and automates data movement and data transformation.

ETL

ETL Data Ingestion Automation

Han Heloir, MongoDB: The role of scalable databases in AI-powered apps

AI News

SEPTEMBER 29, 2024

Here are a few key reasons: The variety and volume of data will continue to grow, requiring the database to handle diverse data types—structured, unstructured, and semi-structured—at scale. Selecting a database that can manage such variety without complex ETL processes is important.

Big Data

Big Data ETL Generative AI Data Ingestion

Basil Faruqui, BMC: Why DataOps needs orchestration to make it work

AI News

AUGUST 29, 2023

If you think about building a data pipeline, whether you’re doing a simple BI project or a complex AI or machine learning project, you’ve got data ingestion, data storage and processing, and data insight – and underneath all of those four stages, there’s a variety of different technologies being used,” explains Faruqui.

Data Ingestion

Data Ingestion Big Data Explainability ETL

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What is Data Ingestion? Understanding the Basics

Pickl AI

JULY 25, 2024

Summary: Data ingestion is the process of collecting, importing, and processing data from diverse sources into a centralised system for analysis. This crucial step enhances data quality, enables real-time insights, and supports informed decision-making. This is where data ingestion comes in.

Data Ingestion

Data Ingestion ETL Data Quality Data Integration

Upstage AI Introduces Dataverse for Addressing Challenges in Data Processing for Large Language Models

Marktechpost

APRIL 1, 2024

Utilizing frameworks like Slurm and Spark enables efficient big data management, while data quality improvements through deduplication, decontamination, and sentence length adjustments refine training datasets. The ETL (Extract, Transform, Load) process is also critical in aggregating and processing data from varied sources.

Large Language Models

Large Language Models ETL Data Ingestion Data Quality

A Comprehensive Overview of Data Engineering Pipeline Tools

Marktechpost

JUNE 13, 2024

Strengths: It offers parallel processing, flexibility, and built-in capabilities for various data tasks, including graph processing. AWS Glue: A serverless ETL service that simplifies the monitoring and management of data pipelines. Strengths: Fault-tolerant, scalable, and reliable for real-time data processing.

ETL

ETL Machine Learning Data Ingestion Big Data

A Simple Guide to Real-Time Data Ingestion

Pickl AI

JULY 24, 2023

What is Real-Time Data Ingestion? Real-time data ingestion is the practise of gathering and analysing information as it is produced, without little to no lag between the emergence of the data and its accessibility for analysis. Traders need up-to-the-second information to make informed decisions.

Data Ingestion

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ETL Process Explained: Essential Steps for Effective Data Management

Pickl AI

OCTOBER 17, 2024

Summary: The ETL process, which consists of data extraction, transformation, and loading, is vital for effective data management. Following best practices and using suitable tools enhances data integrity and quality, supporting informed decision-making. Introduction The ETL process is crucial in modern data management.

ETL

ETL Explainability Data Integration Data Extraction

Improving air quality with generative AI

AWS Machine Learning Blog

JUNE 18, 2024

This manual synchronization process, hindered by disparate data formats, is resource-intensive, limiting the potential for widespread data orchestration. The platform, although functional, deals with CSV and JSON files containing hundreds of thousands of rows from various manufacturers, demanding substantial effort for data ingestion.

Generative AI

Generative AI Data Ingestion Python LLM

A Beginner’s Guide to Data Warehousing

Unite.AI

DECEMBER 5, 2023

They can contain structured, unstructured, or semi-structured data. These can include structured databases, log files, CSV files, transaction tables, third-party business tools, sensor data, etc. The data ecosystem is connected to company-defined data sources that can ingest historical data after a specified period.

Metadata

Metadata Big Data ETL Data Mining

Data architecture strategy for data quality

IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases.

Data Quality

Data Quality Metadata ETL Big Data

Introduction to Apache NiFi and Its Architecture

Pickl AI

JULY 30, 2024

Summary: Apache NiFi is a powerful open-source data ingestion platform design to automate data flow management between systems. Its architecture includes FlowFiles, repositories, and processors, enabling efficient data processing and transformation.

Data Ingestion

Data Ingestion ETL Big Data Data Integration

Build an image search engine with Amazon Kendra and Amazon Rekognition

AWS Machine Learning Blog

MAY 5, 2023

The following figure shows an example diagram that illustrates an orchestrated extract, transform, and load (ETL) architecture solution. For example, searching for the terms “How to orchestrate ETL pipeline” returns results of architecture diagrams built with AWS Glue and AWS Step Functions.

Metadata

Metadata ETL ML Data Ingestion

Build a news recommender application with Amazon Personalize

AWS Machine Learning Blog

APRIL 4, 2024

You can take two different approaches to ingest training data: Batch ingestion – You can use AWS Glue to transform and ingest interactions and items data residing in an Amazon Simple Storage Service (Amazon S3) bucket into Amazon Personalize datasets.

ETL

ETL Auto-complete Metadata Data Ingestion

Differentiation: Microsoft Fabric vs Power BI

Pickl AI

DECEMBER 16, 2024

Its core components include: Lakehouse : Offers robust data storage and processing capabilities. Data Factory : Simplifies the creation of ETL pipelines to integrate data from diverse sources. It supports a broad range of data types and sources, ensuring robust data management across silos.

ETL

ETL Data Ingestion Data Integration Business Intelligence

Unfolding the Details of Hive in Hadoop

Pickl AI

JULY 6, 2023

Thus, making it easier for analysts and data scientists to leverage their SQL skills for Big Data analysis. It applies the data structure during querying rather than data ingestion. How Data Flows in Hive In Hive, data flows through several steps to enable querying and analysis.

Big Data

Big Data ETL Data Analysis Metadata

Popular Data Transformation Tools: Importance and Best Practices

Pickl AI

OCTOBER 10, 2024

Apache Nifi Apache Nifi is an open-source data integration tool that automates system data flow. Its drag-and-drop interface makes it user-friendly, allowing data engineers to build complex workflows without extensive coding knowledge. AWS Glue AWS Glue is a fully managed ETL service provided by Amazon Web Services.

ETL

ETL Data Quality Business Intelligence Big Data

Comparing Tools For Data Processing Pipelines

The MLOps Blog

MARCH 15, 2023

A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.

ETL

ETL Categorization Automation Data Integration

Leveraging Data Engineering to Enhance Customer 360 Initiatives

TransOrg Analytics

AUGUST 21, 2024

The main steps involved were: Data Ingestion: Moving information into Azure Blob Storage and keeping it raw in Azure Synapse. Data Transformation: Using Azure Synapse ETL pipelines for cleansing and transforming data. Data Storage: Keeping altered data within Azure Synapse’s enrichment layer.

Big Data Engineer

Big Data Engineer ETL Data Ingestion Data Integration

Azure Data Engineer Jobs

Pickl AI

APRIL 6, 2023

Answer : Data Masking features available in Azure include Azure SQL Database masking, Dynamic data masking, Azure Data Factory masking, Azure Data Share Masking, and Azure Synapse Analytics masking. Answer : Polybase helps optimize data ingestion into PDW and supports T-SQL. What is Polybase?

ETL

ETL Big Data Data Ingestion Software Engineer

Build Data Pipelines: Comprehensive Step-by-Step Guide

Pickl AI

JULY 8, 2024

Tools such as Python’s Pandas library, Apache Spark, or specialised data cleaning software streamline these processes, ensuring data integrity before further transformation. Step 3: Data Transformation Data transformation focuses on converting cleaned data into a format suitable for analysis and storage.

ETL

ETL Data Quality Data Integration Automation

Unlocking the 12 Ways to Improve Data Quality

Pickl AI

OCTOBER 19, 2023

Data Governance Establish data governance policies to define roles, responsibilities, and data ownership within your organization. ETL (Extract, Transform, Load) Processes Enhance ETL processes to ensure data quality checks are performed during data ingestion.

Data Quality

Data Quality ETL Machine Learning Data Ingestion

Supercharging Your Data Pipeline with Apache Airflow (Part 2)

Heartbeat

NOVEMBER 6, 2023

Image Source — Pixel Production Inc In the previous article, you were introduced to the intricacies of data pipelines, including the two major types of existing data pipelines. You also learned how to build an Extract Transform Load (ETL) pipeline and discovered the automation capabilities of Apache Airflow for ETL pipelines.

ETL

ETL Python Metadata Deep Learning

Drowning in Data? A Data Lake May Be Your Lifesaver

ODSC - Open Data Science

SEPTEMBER 29, 2023

Enterprises using Spark for a data lake implementation need to source and integrate additional software for tools that support user management, data storage and delivery, execution control, and administration. It truly is an all-in-one data lake solution.

Big Data

Big Data ETL Data Science Data Ingestion

How to Build Machine Learning Systems With a Feature Store

The MLOps Blog

JANUARY 26, 2024

Recommended How to Solve the Data Ingestion and Feature Store Component of the MLOps Stack Read more A unified architecture for ML systems One of the challenges in building machine-learning systems is architecting the system. Reference table for which technologies to use for your FTI pipelines for each ML system.

Machine Learning

Machine Learning Metadata ML Python

Building an efficient MLOps platform with OSS tools on Amazon ECS with AWS Fargate

AWS Machine Learning Blog

SEPTEMBER 18, 2024

Hosted on Amazon ECS with tasks run on Fargate, this platform streamlines the end-to-end ML workflow, from data ingestion to model deployment. An example direct acyclic graph (DAG) might automate data ingestion, processing, model training, and deployment tasks, ensuring that each step is run in the correct order and at the right time.

Machine Learning

Machine Learning Data Scientist Data Ingestion ML

TransOrg’s Cloud Data Engineering Services on AWS, GCP & Snowflake

TransOrg Analytics

SEPTEMBER 24, 2024

Data Foundation on AWS Amazon S3: Scalable storage foundation for data lakes. AWS Lake Formation: Simplify the process of creating and managing a secure data lake. Amazon Redshift: Fast, scalable data warehouse for analytics. AWS Glue: Fully managed ETL service for easy data preparation and integration.

ETL

ETL LLM Data Ingestion Automation

TransOrg’s Cloud Data Engineering Services on AWS, GCP & Snowflake

TransOrg Analytics

SEPTEMBER 24, 2024

Data Foundation on AWS Amazon S3: Scalable storage foundation for data lakes. AWS Lake Formation: Simplify the process of creating and managing a secure data lake. Amazon Redshift: Fast, scalable data warehouse for analytics. AWS Glue: Fully managed ETL service for easy data preparation and integration.

ETL

ETL LLM Data Ingestion Automation

Driving Progress with Open Data Science: Trends, Tools, and Opportunities

ODSC - Open Data Science

DECEMBER 9, 2024

It enables accessing, transforming, analyzing, and visualizing data on a single workstation. Databricks offers a cloud-based platform optimized for data engineering and collaborative analytics at scale. It brings together data ingestion, transformation, model training, and deployment in one integrated workflow.

Data Science

Data Science Data Scientist Python Machine Learning

Artificial Intelligence Zone

Most Frequently Asked Azure Data Factory Interview Questions

Han Heloir, MongoDB: The role of scalable databases in AI-powered apps

Webinars

Trending Sources

Basil Faruqui, BMC: Why DataOps needs orchestration to make it work

Webinars

What is Data Ingestion? Understanding the Basics

Upstage AI Introduces Dataverse for Addressing Challenges in Data Processing for Large Language Models

A Comprehensive Overview of Data Engineering Pipeline Tools

A Simple Guide to Real-Time Data Ingestion

ETL Process Explained: Essential Steps for Effective Data Management

Improving air quality with generative AI

A Beginner’s Guide to Data Warehousing

Data architecture strategy for data quality

Introduction to Apache NiFi and Its Architecture

Build an image search engine with Amazon Kendra and Amazon Rekognition

Build a news recommender application with Amazon Personalize

Differentiation: Microsoft Fabric vs Power BI

Unfolding the Details of Hive in Hadoop

Popular Data Transformation Tools: Importance and Best Practices

Comparing Tools For Data Processing Pipelines

Leveraging Data Engineering to Enhance Customer 360 Initiatives

Azure Data Engineer Jobs

Build Data Pipelines: Comprehensive Step-by-Step Guide

Unlocking the 12 Ways to Improve Data Quality

Supercharging Your Data Pipeline with Apache Airflow (Part 2)

Drowning in Data? A Data Lake May Be Your Lifesaver

How to Build Machine Learning Systems With a Feature Store

Building an efficient MLOps platform with OSS tools on Amazon ECS with AWS Fargate

TransOrg’s Cloud Data Engineering Services on AWS, GCP & Snowflake

TransOrg’s Cloud Data Engineering Services on AWS, GCP & Snowflake

Driving Progress with Open Data Science: Trends, Tools, and Opportunities

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