IBM Journey to AI blog

MAY 15, 2024

Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges. ETL/ELT tools typically have two components: a design time (to design data integration jobs) and a runtime (to execute data integration jobs).

Data Pipeline 85

Data Pipeline ETL SQL Database 85

Precisely

JANUARY 9, 2023

With global data creation projected to grow to more than 180 zettabytes by 2025 , it’s not surprising that more organizations than ever are looking to harness their ever-growing datasets to drive more confident business decisions. As data initiatives become more sophisticated, organizations will uncover new data quality challenges.

Data Governance 81

Data Governance Data Quality Data Observability Data Pipeline 81

Pickl AI

APRIL 9, 2025

It provides insights into considerations for choosing the right tool, ensuring businesses can optimize their data integration processes for better analytics and decision-making. Introduction In todays data-driven world, organizations are overwhelmed with vast amounts of information.

ETL 52

ETL Data Warehouse AWS Business Intelligence 52

Pickl AI

DECEMBER 9, 2024

Introduction Big Data continues transforming industries, making it a vital asset in 2025. The global Big Data Analytics market, valued at $307.51 Key challenges include data storage, processing speed, scalability, and security and compliance. What is the Role of Zookeeper in Big Data? What is Schema-on-read?

Big Data 52

Big Data Big Data Hadoop Apache Kafka 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 Engineer Data Engineering 52

Precisely

JUNE 2, 2023

Gartner estimates that 85% percent of organizations plan to fully embrace a cloud-first strategy by 2025. Insufficient skills, limited budgets, and poor data quality also present significant challenges. Together with other data integrity tools, you can maintain the accuracy, completeness, and quality of data over its lifecycle.

Data Silos 52

Data Silos ETL Data Quality Data Pipeline 52

IBM Journey to AI blog

OCTOBER 12, 2023

Securing AI models and their access to data While AI models need flexibility to access data across a hybrid infrastructure, they also need safeguarding from tampering (unintentional or otherwise) and, especially, protected access to data.

AI 93

AI AI Data Scientist Data Governance 93