IBM Journey to AI blog

JANUARY 5, 2023

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Data Science Dojo

JULY 6, 2023

These tools provide data engineers with the necessary capabilities to efficiently extract, transform, and load (ETL) data, build data pipelines, and prepare data for analysis and consumption by other applications. It allows data engineers to define and manage complex workflows as directed acyclic graphs (DAGs).

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The MLOps Blog

MAY 17, 2023

However, efficient use of ETL pipelines in ML can help make their life much easier. This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines.

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Pickl AI

NOVEMBER 4, 2024

Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

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AWS Machine Learning Blog

NOVEMBER 12, 2024

Their data pipeline (as shown in the following architecture diagram) consists of ingestion, storage, ETL (extract, transform, and load), and a data governance layer. Multi-source data is initially received and stored in an Amazon Simple Storage Service (Amazon S3) data lake.

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Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning. AWS Glue AWS Glue is a fully managed ETL service provided by Amazon Web Services.

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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. Data Lakes allow for flexible analysis.

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Pickl AI

JUNE 28, 2023

Data cleaning, normalization, and reformatting to match the target schema is used. · Data Loading It is the final step where transformed data is loaded into a target system, such as a data warehouse or a data lake. It ensures that the integrated data is available for analysis and reporting.

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DagsHub

NOVEMBER 11, 2024

In general, this data has no clear structure because it may manifest real-world complexity, such as the subtlety of language or the details in a picture. Advanced methods are needed to process unstructured data, but its unstructured nature comes from how easily it is made and shared in today's digital world.

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IBM Journey to AI blog

JULY 3, 2024

To power AI and analytics workloads across your transactional and purpose-built databases, you must ensure they can seamlessly integrate with an open data lakehouse architecture without duplication or additional extract, transform, load (ETL) processes. Effective data quality management is crucial to mitigating these risks.

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Precisely

JUNE 6, 2023

The group kicked off the session by exchanging ideas about what it means to have a modern data architecture. Atif Salam noted that as recently as a year ago, the primary focus in many organizations was on ingesting data and building data lakes.

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IBM Journey to AI blog

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

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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.

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AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

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Dataconomy

SEPTEMBER 4, 2023

Understand what insights you need to gain from your data to drive business growth and strategy. Best practices in cloud analytics are essential to maintain data quality, security, and compliance ( Image credit ) Data governance: Establish robust data governance practices to ensure data quality, security, and compliance.

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Alation

MAY 16, 2023

Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible. Data Quality When using a data pipeline, data consistency, quality, and reliability are often greatly improved.

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DagsHub

OCTOBER 23, 2024

To combine the collected data, you can integrate different data producers into a data lake as a repository. A central repository for unstructured data is beneficial for tasks like analytics and data virtualization. Data Cleaning The next step is to clean the data after ingesting it into the data lake.

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Dataconomy

FEBRUARY 23, 2023

Data engineers play a crucial role in managing and processing big data Ensuring data quality and integrity Data quality and integrity are essential for accurate data analysis. Data engineers are responsible for ensuring that the data collected is accurate, consistent, and reliable.

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ODSC - Open Data Science

APRIL 3, 2023

Skills like effective verbal and written communication will help back up the numbers, while data visualization (specific frameworks in the next section) can help you tell a complete story. Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis.

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Pickl AI

AUGUST 9, 2024

Data Integration Tools Technologies such as Apache NiFi and Talend help in the seamless integration of data from various sources into a unified system for analysis. Understanding ETL (Extract, Transform, Load) processes is vital for students. Students should learn about data wrangling and the importance of data quality.

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The MLOps Blog

MARCH 15, 2023

Data Processing : You need to save the processed data through computations such as aggregation, filtering and sorting. Data Storage : To store this processed data to retrieve it over time – be it a data warehouse or a data lake. This ensures that the data is accurate, consistent, and reliable.

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phData

FEBRUARY 5, 2024

Setting up the Information Architecture Setting up an information architecture during migration to Snowflake poses challenges due to the need to align existing data structures, types, and sources with Snowflake’s multi-cluster, multi-tier architecture. Moving historical data from a legacy system to Snowflake poses several challenges.

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Alation

MARCH 22, 2022

As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. It synthesizes all we’ve learned about agile, data quality , and ETL/ELT. DataOps has emerged as an exciting solution. As pressures to modernize mount, the promise of DataOps has attracted attention.

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Alation

MAY 16, 2023

Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible. Data Quality When using a data pipeline, data consistency, quality, and reliability are often greatly improved.

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phData

SEPTEMBER 27, 2024

If the event log is your customer’s diary, think of persistent staging as their scrapbook – a place where raw customer data is collected, organized, and kept for future reference. In traditional ETL (Extract, Transform, Load) processes in CDPs, staging areas were often temporary holding pens for data.

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Data Models Data Modeling Apache Kafka Data Lakes 52

Pickl AI

JUNE 11, 2024

Let’s delve into the key components that form the backbone of a data warehouse: Source Systems These are the operational databases, CRM systems, and other applications that generate the raw data feeding the data warehouse. Data Extraction, Transformation, and Loading (ETL) This is the workhorse of architecture.

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Alation

FEBRUARY 20, 2020

Data lakes, while useful in helping you to capture all of your data, are only the first step in extracting the value of that data. Additionally, because of the collaborative features found in the Alation Data Catalog, you also gain the ability for data to be easily shared, used and reused.

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IBM Journey to AI blog

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. Then, it applies these insights to automate and orchestrate the data lifecycle.

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