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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AWS Database ETL AI 93

Pickl AI

OCTOBER 6, 2024

Summary: This blog explores the key differences between ETL and ELT, detailing their processes, advantages, and disadvantages. Understanding these methods helps organizations optimize their data workflows for better decision-making. What is ETL? ETL stands for Extract, Transform, and Load.

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ETL Data Warehouse Data Quality Data Lakes 52

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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ETL Data Warehouse Data Quality Data Governance 52

Precisely

JANUARY 9, 2025

The magic of the data warehouse was figuring out how to get data out of these transactional systems and reorganize it in a structured way optimized for analysis and reporting. Which turned into data lakes and data lakehouses Poor data quality turned Hadoop into a data swamp, and what sounds better than a data swamp?

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Data Warehouse Hadoop Data Lakes Data Governance 119

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. What is ETL? ETL stands for Extract, Transform, Load.

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ETL Data Warehouse SQL Data Quality 52

Pickl AI

OCTOBER 15, 2024

Summary: Selecting the right ETL platform is vital for efficient data integration. Consider your business needs, compare features, and evaluate costs to enhance data accuracy and operational efficiency. Introduction In today’s data-driven world, businesses rely heavily on ETL platforms to streamline data integration processes.

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ETL Azure AWS Data Governance 52

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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ETL Data Pipeline ML ML 59

IBM Data Science in Practice

DECEMBER 7, 2022

IBM Multicloud Data Integration helps organizations connect data from disparate sources, build data pipelines, remediate data issues, enrich data, and deliver integrated data to multicloud platforms where it can easily accessed by data consumers or built into a data product.

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Data Pipeline Data Quality Data Preparation ETL 130

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 Quality Data Lakes Data Warehouse Big Data 52

Pickl AI

OCTOBER 19, 2023

Data quality plays a significant role in helping organizations strategize their policies that can keep them ahead of the crowd. Hence, companies need to adopt the right strategies that can help them filter the relevant data from the unwanted ones and get accurate and precise output.

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Data Quality Data Governance Data Warehouse Machine Learning 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. Also Read: Top 10 Data Science tools for 2024.

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ETL Data Quality Data Pipeline Data Warehouse 40

Dataversity

AUGUST 30, 2021

DataOps, which focuses on automated tools throughout the ETL development cycle, responds to a huge challenge for data integration and ETL projects in general. ETL projects are increasingly based on agile processes and automated testing. extract, transform, load) projects are often devoid of automated testing.

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DataOps ETL Data Pipeline Data Warehouse 52

IBM Data Science in Practice

NOVEMBER 28, 2022

IBM Multicloud Data Integration helps organizations connect data from disparate sources, build data pipelines, remediate data issues, enrich data, and deliver integrated data to multicloud platforms where it can easily accessed by data consumers or built into a data product.

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Data Pipeline Data Quality Data Preparation Data Governance 130

Data Science Blog

JUNE 27, 2023

The batch views within the Lambda architecture allow for the application of more complex or resource-intensive rules, resulting in superior data quality and reduced bias over time. On the other hand, the real-time views provide immediate access to the most current data.

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Big Data Big Data Apache Kafka Database 130

Pickl AI

DECEMBER 25, 2024

As a result, data silos create barriers that prevent seamless access to information across an organisation. Data silos typically arise in large enterprises where different departments operate autonomously. For instance, a sales department may maintain its own database that is incompatible with the accounting department’s system.

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Data Silos Database Data Quality ETL 52

Pickl AI

AUGUST 31, 2023

However, analysis of data may involve partiality or incorrect insights in case the data quality is not adequate. Accordingly, the need for Data Profiling in ETL becomes important for ensuring higher data quality as per business requirements. What is Data Profiling in ETL?

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Data Profiling ETL Data Quality Data Wrangling 52

Smart Data Collective

APRIL 29, 2020

The storage and processing of data through a cloud-based system of applications. Master data management. The techniques for managing organisational data in a standardised approach that minimises inefficiency. Extraction, Transform, Load (ETL). Data transformation. Microsoft Azure.

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Data Warehouse SQL Azure ETL 97

ODSC - Open Data Science

SEPTEMBER 27, 2023

In this article, we will delve into the concept of data lakes, explore their differences from data warehouses and relational databases, and discuss the significance of data version control in the context of large-scale data management. This ensures data consistency and integrity.

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Data Lakes Data Warehouse Database Big Data 98

IBM Journey to AI blog

APRIL 20, 2023

This can ensure that the decisions made are reliable and of high quality. Before a bank can start the process of certifying a risk model, they first need to understand what data is being used and how it changes as it moves from a database to a model.

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Database Data Engineering Data Engineer Data Engineering 86

Dataconomy

SEPTEMBER 4, 2023

Cloud-based business intelligence (BI): Cloud-based BI tools enable organizations to access and analyze data from cloud-based sources and on-premises databases. Understand what insights you need to gain from your data to drive business growth and strategy. Ensure that data is clean, consistent, and up-to-date.

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Analytics Analytics Big Data Analytics Big Data Analytics 203

Pickl AI

JUNE 28, 2023

Schema Integration Schema integration deals with reconciling data stored in different database schemas or structures. It involves mapping and transforming data elements to align with a unified schema. It requires human effort to extract data from each source and merge it.

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Data Mining Data Mining Data Mining ETL 52

Dataversity

SEPTEMBER 21, 2021

In my first business intelligence endeavors, there were data normalization issues; in my Data Governance period, Data Quality and proactive Metadata Management were the critical points. The post The Declarative Approach in a Data Playground appeared first on DATAVERSITY. It is something so simple and so powerful.

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Data Governance Business Intelligence Business Intelligence Data Quality 98

IBM Journey to AI blog

OCTOBER 22, 2024

The ability to effectively deploy AI into production rests upon the strength of an organization’s data strategy because AI is only as strong as the data that underpins it. This strategy helps organizations optimize data usage, expand into new markets, and increase revenue.

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Data Silos Data Pipeline DataOps Business Intelligence 102

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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Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

JUNE 11, 2024

Summary: A data warehouse is a central information hub that stores and organizes vast amounts of data from different sources within an organization. Unlike operational databases focused on daily tasks, data warehouses are designed for analysis, enabling historical trend exploration and informed decision-making.

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Data Warehouse ETL Data Mining Data Mining 52

IBM Journey to AI blog

JULY 3, 2024

Modernizing your data infrastructure to hybrid cloud for applications, analytics and gen AI Adopting multicloud and hybrid strategies is becoming mandatory, requiring databases that support flexible deployments across the hybrid cloud. This ensures you have a data foundation that grows with your data needs, wherever your data resides.

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AI AI Data Quality Database 45

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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Data Quality AWS Machine Learning Machine Learning 52

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. Files: Data stored in flat files, CSVs, or Excel sheets.

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Apache Kafka Data Lakes Data Warehouse Data Quality 52

Tableau

AUGUST 21, 2023

Catalog Enhanced data trust, visibility, and discoverability Tableau Catalog automatically catalogs all your data assets and sources into one central list and provides metadata in context for fast data discovery. Included with Data Management. database tables and columns). table or workbook).

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Tableau Database Analytics Analytics 97

Pickl AI

NOVEMBER 15, 2023

What Is Data Lake? A Data Lake is a centralized repository that allows businesses to store vast volumes of structured and unstructured data at any scale. Unlike traditional databases, Data Lakes enable storage without the need for a predefined schema, making them highly flexible.

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Data Lakes Data Warehouse Database ETL 52

phData

JUNE 23, 2023

Data lineage is essential for several reasons: Data Governance – Data lineage enables organizations to track data usage, ensure compliance with regulations, and understand the impact of data changes. The ACCESS_HISTORY view is created within the SNOWFLAKE database in the ACCOUNT_USAGE schema.

Data Quality 52

Data Quality Data Governance ETL Database 52

Mlearning.ai

AUGUST 10, 2023

The project I did to land my business intelligence internship — CAR BRAND SEARCH ETL PROCESS WITH PYTHON, POSTGRESQL & POWER BI 1. Section 2: Explanation of the ETL diagram for the project. Section 4: Reporting data for the project insights. ETL ARCHITECTURE DIAGRAM ETL stands for Extract, Transform, Load.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Power BI 52

Precisely

JUNE 2, 2023

As companies strive to leverage AI/ML, location intelligence, and cloud analytics into their portfolio of tools, siloed mainframe data often stands in the way of forward momentum. Insufficient skills, limited budgets, and poor data quality also present significant challenges. To learn more, read our ebook.

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Data Silos ETL Data Quality Data Pipeline 52

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 Warehousing: Amazon Redshift, Google BigQuery, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

JULY 8, 2024

Organisations leverage diverse methods to gather data, including: Direct Data Capture: Real-time collection from sensors, devices, or web services. Database Extraction: Retrieval from structured databases using query languages like SQL. Aggregation: Summarising data into meaningful metrics or aggregates.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

Pickl AI

MARCH 3, 2025

Essentially, BI bridges the gap between raw data and actionable knowledge. It gathers information from various sources sales databases, marketing platforms, customer feedback, and more and consolidates it into a unified view. Ensuring data accuracy and consistency through cleansing and validation processes.

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Business Intelligence Business Intelligence Data Analyst Data Visualization 52

Mlearning.ai

MAY 16, 2023

This article discusses five commonly used architectural design patterns in data engineering and their use cases. ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. Finally, the transformed data is loaded into the target system.

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Data Engineering Data Engineering Data Engineering Data Engineer 52