IBM Journey to AI blog

MAY 15, 2024

Two of the more popular methods, extract, transform, load (ETL ) and extract, load, transform (ELT) , are both highly performant and scalable. Data engineers build data pipelines, which are called data integration tasks or jobs, as incremental steps to perform data operations and orchestrate these data pipelines in an overall workflow.

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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.

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Data Science Dojo

JULY 6, 2023

Data engineering tools are software applications or frameworks specifically designed to facilitate the process of managing, processing, and transforming large volumes of data. Airflow: Apache Airflow is an open-source platform for orchestrating and scheduling data pipelines.

Data Engineering 195

Data Engineering Data Engineering Data Engineering Data Engineer 195

Pickl AI

APRIL 9, 2025

Summary: This guide explores the top list of ETL tools, highlighting their features and use cases. It provides insights into considerations for choosing the right tool, ensuring businesses can optimize their data integration processes for better analytics and decision-making. What is ETL? What are ETL Tools?

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phData

MARCH 14, 2024

In the data analytics processes, choosing the right tools is crucial for ensuring efficiency and scalability. Two popular players in this area are Alteryx Designer and Matillion ETL , both offering strong solutions for handling data workflows with Snowflake Data Cloud integration.

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Pickl AI

JULY 8, 2024

Summary: This blog explains how to build efficient data pipelines, detailing each step from data collection to final delivery. Introduction Data pipelines play a pivotal role in modern data architecture by seamlessly transporting and transforming raw data into valuable insights.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

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.

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Pickl AI

OCTOBER 17, 2024

Summary: This article explores the significance of ETL Data in Data Management. It highlights key components of the ETL process, best practices for efficiency, and future trends like AI integration and real-time processing, ensuring organisations can leverage their data effectively for strategic decision-making.

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Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Choosing the right ETL tool is crucial for smooth data management.

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Flipboard

NOVEMBER 24, 2023

This use case highlights how large language models (LLMs) are able to become a translator between human languages (English, Spanish, Arabic, and more) and machine interpretable languages (Python, Java, Scala, SQL, and so on) along with sophisticated internal reasoning. Room for improvement!

Database 158

Database AWS ETL SQL 158

phData

JUNE 14, 2023

In recent years, data engineering teams working with the Snowflake Data Cloud platform have embraced the continuous integration/continuous delivery (CI/CD) software development process to develop data products and manage ETL/ELT workloads more efficiently.

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Data Pipeline Database SQL Data Engineering 52

phData

JULY 12, 2023

Unlike traditional methods that rely on complex SQL queries for orchestration, Matillion Jobs provides a more streamlined approach. By converting SQL scripts into Matillion Jobs , users can take advantage of the platform’s advanced features for job orchestration, scheduling, and sharing. What is Matillion ETL?

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phData

APRIL 21, 2023

Unlike traditional methods that rely on complex SQL queries for orchestration, Matillion Jobs provide a more streamlined approach. By converting SQL scripts into Matillion Jobs , users can take advantage of the platform’s advanced features for job orchestration, scheduling, and sharing. In our case, this table is “orders.”

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SQL ETL Database Data Pipeline 52

Applied Data Science

AUGUST 2, 2021

Automation Automating data pipelines and models ➡️ 6. The most common data science languages are Python and R   —  SQL is also a must have skill for acquiring and manipulating data. The Data Engineer Not everyone working on a data science project is a data scientist.

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Data Science Dojo

JULY 3, 2024

Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python. Databases and SQL : Managing and querying relational databases using SQL, as well as working with NoSQL databases like MongoDB.

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The MLOps Blog

MARCH 15, 2023

In this post, you will learn about the 10 best data pipeline tools, their pros, cons, and pricing. 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.

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Data Pipeline ETL SQL Data Quality 59

phData

MARCH 8, 2023

It allows organizations to easily connect their disparate data sources without having to manage any infrastructure. Fivetran’s automated data movement platform simplifies the ETL (extract, transform, load) process by automating most of the time-consuming tasks of ETL that data engineers would typically do.

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Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

phData

SEPTEMBER 2, 2024

Best practices are a pivotal part of any software development, and data engineering is no exception. This ensures the data pipelines we create are robust, durable, and secure, providing the desired data to the organization effectively and consistently. What Are Matillion Jobs and Why Do They Matter?

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phData

AUGUST 8, 2024

Putting the T for Transformation in ELT (ETL) is essential to any data pipeline. After extracting and loading your data into the Snowflake AI Data Cloud , you may wonder how best to transform it. What are Snowflake’s Best Features for Data Transformation?

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Women in Big Data

NOVEMBER 27, 2024

Evaluate integration capabilities with existing data sources and Extract Transform and Load (ETL) tools. Its PostgreSQL foundation ensures compatibility with most SQL clients. Strengths : Real-time analytics, built-in machine learning capabilities, and fast querying with standard SQL.

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The MLOps Blog

SEPTEMBER 7, 2023

Data Scientists and ML Engineers typically write lots and lots of code. From writing code for doing exploratory analysis, experimentation code for modeling, ETLs for creating training datasets, Airflow (or similar) code to generate DAGs, REST APIs, streaming jobs, monitoring jobs, etc. Related post MLOps Is an Extension of DevOps.

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phData

APRIL 28, 2025

For those unfamiliar with GIT or GIT practices, please refer Git for Business Users with Matillion DPC What is a Matillion Pipeline? A Matillion pipeline is a collection of jobs that extract, load, and transform (ETL/ELT) data from various sources into a target system, such as a cloud data warehouse like Snowflake.

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phData

JULY 17, 2023

In order to fully leverage this vast quantity of collected data, companies need a robust and scalable data infrastructure to manage it. This is where Fivetran and the Modern Data Stack come in. This complexity often requires many hours of work from a large data engineering team to build and manually manage data pipelines.

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Data Warehouse Data Pipeline Cloud Data ETL 52

Pickl AI

NOVEMBER 4, 2024

Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. ETL is vital for ensuring data quality and integrity.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

phData

APRIL 29, 2024

Understanding Fivetran Fivetran is a popular Software-as-a-Service platform that enables users to automate the movement of data and ETL processes across diverse sources to a target destination. For a longer overview, along with insights and best practices, please feel free to jump back to the previous blog.

SQL 52

SQL Data Warehouse Azure Cloud Data 52

Pickl AI

JULY 3, 2023

Here are steps you can follow to pursue a career as a BI Developer: Acquire a solid foundation in data and analytics: Start by building a strong understanding of data concepts, relational databases, SQL (Structured Query Language), and data modeling.

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

ODSC - Open Data Science

JANUARY 18, 2024

This individual is responsible for building and maintaining the infrastructure that stores and processes data; the kinds of data can be diverse, but most commonly it will be structured and unstructured data. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

phData

AUGUST 10, 2023

That said, dbt provides the ability to generate data vault models and also allows you to write your data transformations using SQL and code-reusable macros powered by Jinja2 to run your data pipelines in a clean and efficient way. The most important reason for using DBT in Data Vault 2.0

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SQL Data Observability Data Quality Data Pipeline 52

phData

OCTOBER 17, 2023

The story is all too common – a business user requests some data, the data team creates/prioritizes a ticket, and said ticket is completed after some number of months (or weeks if you’re lucky) – just to have the data be wrong, and the whole process starts again. Those are scary for data teams to change.

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ETL Data Pipeline Data Engineering Data Engineer 52

phData

OCTOBER 20, 2023

Some of the databases supported by Fivetran are: Snowflake Data Cloud (BETA) MySQL PostgreSQL SAP ERP SQL Server Oracle In this blog, we will review how to pull Data from on-premise Systems using Fivetran to a specific target or destination. Databases often write more information to a transaction log than is required.

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Database SQL ETL Data Warehouse 52

Alation

JANUARY 17, 2023

It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Reverse ETL tools. Data orchestration tools. A Note on the Shift from ETL to ELT.

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Data Warehouse ETL Tableau Cloud Data 52

Alation

JUNE 14, 2023

You don’t have to write ETL jobs.” That lowers the barrier to entry because you don’t have to be an ETL developer. Data Pipeline Capabilities This team’s scope is massive because the data pipelines are huge and there are many different capabilities embedded in them. Invest in automation.

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Data Pipeline ETL Data Warehouse SQL 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

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. Though it’s worth mentioning that Airflow isn’t used at runtime as is usual for extract, transform, and load (ETL) tasks.

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phData

MARCH 1, 2024

There’s no need for developers or analysts to manually adjust table schemas or modify ETL (Extract, Transform, Load) processes whenever the source data structure changes. Time Efficiency – The automated schema detection and evolution features contribute to faster data availability.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

phData

FEBRUARY 7, 2024

Snowpark is the set of libraries and runtimes in Snowflake that securely deploy and process non-SQL code, including Python, Java, and Scala. A DataFrame is like a query that must be evaluated to retrieve data. An action causes the DataFrame to be evaluated and sends the corresponding SQL statement to the server for execution.

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