Big Data, Data Ingestion and Data Integration - Artificial Intelligence Zone

Big Data

Data Ingestion

Data Integration

A Comprehensive Overview of Data Engineering Pipeline Tools

Marktechpost

JUNE 13, 2024

ELT Pipelines: Typically used for big data, these pipelines extract data, load it into data warehouses or lakes, and then transform it. It is suitable for distributed and scalable large-scale data processing, providing quick big-data query and analysis capabilities.

ETL

ETL Machine Learning Data Ingestion Big Data

A Beginner’s Guide to Data Warehousing

Unite.AI

DECEMBER 5, 2023

In this digital economy, data is paramount. Today, all sectors, from private enterprises to public entities, use big data to make critical business decisions. However, the data ecosystem faces numerous challenges regarding large data volume, variety, and velocity. Enter data warehousing!

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Big Data as a Service (BDaaS): A Comprehensive Overview

Pickl AI

SEPTEMBER 11, 2024

Summary: Big Data as a Service (BDaaS) offers organisations scalable, cost-effective solutions for managing and analysing vast data volumes. By outsourcing Big Data functionalities, businesses can focus on deriving insights, improving decision-making, and driving innovation while overcoming infrastructure complexities.

Big Data

Big Data Data Integration Machine Learning Data Ingestion

Data architecture strategy for data quality

IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

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. What is Apache NiFi?

Data Ingestion

Data Ingestion ETL Big Data Data Integration

Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning Blog

OCTOBER 2, 2024

However, scaling up generative AI and making adoption easier for different lines of businesses (LOBs) comes with challenges around making sure data privacy and security, legal, compliance, and operational complexities are governed on an organizational level. Tanvi Singhal is a Data Scientist within AWS Professional Services.

Generative AI

Generative AI Data Ingestion AI AI

Popular Data Transformation Tools: Importance and Best Practices

Pickl AI

OCTOBER 10, 2024

Introduction Data transformation plays a crucial role in data processing by ensuring that raw data is properly structured and optimised for analysis. Data transformation tools simplify this process by automating data manipulation, making it more efficient and reducing errors.

ETL

ETL Data Quality Business Intelligence Big Data

10 Best Data Engineering Books [Beginners to Advanced]

Pickl AI

AUGUST 1, 2023

The key sectors where Data Engineering has a major contribution include IT, Internet/eCommerce, and Banking & Insurance. Salary of a Data Engineer ranges between ₹ 3.1 Data Storage: Storing the collected data in various storage systems, such as relational databases, NoSQL databases, data lakes, or data warehouses.

Big Data

Big Data Data Analysis Data Scientist Data Ingestion

Leveraging Data Engineering to Enhance Customer 360 Initiatives

TransOrg Analytics

AUGUST 21, 2024

Such success stories have largely depended on Data Engineering processes. This article explores how data engineering can improve Customer 360 initiatives for AWS data engineering , big data engineering, and data analytics companies. Tools Used: AWS glue for data integration and transformation.

ETL

ETL Big Data Engineer Data Ingestion Data Integration

MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

JUNE 27, 2023

Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.

Machine Learning

Machine Learning Metadata Data Scientist Data Quality

How Can The Adoption of a Data Platform Simplify Data Governance For An Organization?

Pickl AI

APRIL 14, 2023

In addition, it also defines the framework wherein it is decided what action needs to be taken on certain data. And so, a company dealing in Big Data Analysis needs to follow stringent Data Governance policies. The same applies to data. What is Data Management? Wrapping it up !!!

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Your Complete Roadmap to Become an Azure Data Scientist

Pickl AI

SEPTEMBER 5, 2024

Unified Data Services: Azure Synapse Analytics combines big data and data warehousing, offering a unified analytics experience. Azure’s global network of data centres ensures high availability and performance, making it a powerful platform for Data Scientists to leverage for diverse data-driven projects.

Data Scientist

Data Scientist Data Science Machine Learning Data Analysis

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

Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

AWS Machine Learning Blog

OCTOBER 23, 2024

By processing data closer to where it resides, SnapLogic promotes faster, more efficient operations that meet stringent regulatory requirements, ultimately delivering a superior experience for businesses relying on their data integration and management solutions. He currently is working on Generative AI for data integration.

Generative AI

Generative AI IDP LLM Automation

A Comprehensive Overview of Data Engineering Pipeline Tools

A Beginner’s Guide to Data Warehousing

Trending Sources

Big Data as a Service (BDaaS): A Comprehensive Overview

Data architecture strategy for data quality

Introduction to Apache NiFi and Its Architecture

Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

Popular Data Transformation Tools: Importance and Best Practices

10 Best Data Engineering Books [Beginners to Advanced]

Leveraging Data Engineering to Enhance Customer 360 Initiatives

MLOps Landscape in 2023: Top Tools and Platforms

How Can The Adoption of a Data Platform Simplify Data Governance For An Organization?

Your Complete Roadmap to Become an Azure Data Scientist

Comparing Tools For Data Processing Pipelines

Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

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