Alation

APRIL 25, 2023

It’d be difficult to exaggerate the importance of data in today’s global marketplace, especially for firms which are going through digital transformation (DT). Using bad data, or the incorrect data can generate devastating results. It can also help you gain key insights so you can make the most out of the data you have.

Data Governance 40

Data Governance ETL Machine Learning Machine Learning 40

ODSC - Open Data Science

JUNE 19, 2023

There is no doubt that real-time operating systems (RTOS) have an important role in the future of big data collection and processing. How does RTOS help advance big data processing? In particular, its progress depends on the availability of related technologies that make the handling of huge volumes of data possible.

Big Data 96

Big Data Big Data Artificial Intelligence Artificial Intelligence 96

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.

AWS 93

AWS Database ETL AI 93

IBM Journey to AI blog

APRIL 20, 2023

This trust depends on an understanding of the data that inform risk models: where does it come from, where is it being used, and what are the ripple effects of a change? Moreover, banks must stay in compliance with industry regulations like BCBS 239, which focus on improving banks’ risk data aggregation and risk reporting capabilities.

Database 86

Database Data Engineering Data Engineer Data Engineering 86

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.

Data Warehouse 52

Data Warehouse ETL Data Mining Data Mining 52

IBM Journey to AI blog

AUGUST 4, 2023

Data democratization instead refers to the simplification of all processes related to data, from storage architecture to data management to data security. It also requires an organization-wide data governance approach, from adopting new types of employee training to creating new policies for data storage.

Data Lakes 73

Data Lakes AI AI Data Governance 73

Alation

MAY 16, 2023

This makes it easier to compare and contrast information and provides organizations with a unified view of their data. Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible.

Data Pipeline 69

Data Pipeline Data Governance Data Lakes Data Warehouse 69

IBM Journey to AI blog

JUNE 29, 2023

Data integration and automation To ensure seamless data integration, organizations need to invest in data integration and automation tools. These tools enable the extraction, transformation, and loading (ETL) of data from various sources.

Analytics 94

Analytics Analytics Business Intelligence Business Intelligence 94

Precisely

MAY 23, 2024

The sudden popularity of cloud data platforms like Databricks , Snowflake , Amazon Redshift, Amazon RDS, Confluent Cloud , and Azure Synapse has accelerated the need for powerful data integration tools that can deliver large volumes of information from transactional applications to the cloud reliably, at scale, and in real time.

Data Governance 64

Data Governance ETL Data Pipeline Azure 64

ODSC - Open Data Science

JUNE 12, 2023

Then we have some other ETL processes to constantly land the past 5 years of data into the Datamarts. Then we have some other ETL processes to constantly land the past 5 years of data into the Datamarts.

Power BI 52

Power BI Data Warehouse ETL Data Preparation 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. Business intelligence (BI) platforms.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

ODSC - Open Data Science

SEPTEMBER 27, 2023

Data Warehouses and Relational Databases It is essential to distinguish data lakes from data warehouses and relational databases, as each serves different purposes and has distinct characteristics. Schema Enforcement: Data warehouses use a “schema-on-write” approach.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

Precisely

MAY 31, 2023

Business and IT teams need a simple, seamless data integrity solution that enables them to: Collaborate on making data accessible Ensure and maintain its quality Enrich the data with third-party context Real-time Access to Data In the past, businesses struggled to get timely access to data for both analytical and operational use cases.

Data Quality 52

Data Quality Data Governance Data Observability ETL 52

phData

APRIL 18, 2023

Snowflake enables organizations to instantaneously scale to meet SLAs with timely delivery of regulatory obligations like SEC Filings, MiFID II, Dodd-Frank, FRTB, or Basel III—all with a single copy of data enabled by data sharing capabilities across various internal departments.

Data Silos 52

Data Silos ETL Clustering Analytics 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 Engineer Data Engineering Data Engineering 52

Pickl AI

OCTOBER 10, 2024

Support for Advanced Analytics : Transformed data is ready for use in Advanced Analytics, Machine Learning, and Business Intelligence applications, driving better decision-making. Using data transformation tools is key to staying competitive in a data-driven world, offering both efficiency and reliability.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

ODSC - Open Data Science

JANUARY 18, 2024

This means that not only do the proper infrastructures need to be created, and maintained, but data engineers will be at the forefront of data governance and access to ensure that no outside actors or black hats gain access which could spell compliance doom for any company.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Precisely

MAY 23, 2024

The sudden popularity of cloud data platforms like Databricks , Snowflake , Amazon Redshift, Amazon RDS, Confluent Cloud , and Azure Synapse has accelerated the need for powerful data integration tools that can deliver large volumes of information from transactional applications to the cloud reliably, at scale, and in real time.

Data Governance 52

Data Governance ETL Data Pipeline Azure 52

Pickl AI

DECEMBER 10, 2024

A unified data fabric also enhances data security by enabling centralised governance and compliance management across all platforms. Automated Data Integration and ETL Tools The rise of no-code and low-code tools is transforming data integration and Extract, Transform, and Load (ETL) processes.

Analytics 52

Analytics Analytics Augmented Analytics Machine Learning 52

Alation

MAY 16, 2023

This makes it easier to compare and contrast information and provides organizations with a unified view of their data. Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible.

Data Pipeline 40

Data Pipeline Data Governance Data Lakes Data Warehouse 40

The Data Administration Newsletter

MARCH 20, 2024

Organizations often struggle with finding nuggets of information buried within their data to achieve their business goals. Technology sometimes comes along to offer some interesting solutions that can bridge that gap for teams that practice good data management hygiene.

Data Governance 105

Data Governance ETL Big Data Big Data 105

Pickl AI

APRIL 26, 2024

The mode is the value that appears most frequently in a data set. Machine learning is a subset of artificial intelligence that enables computers to learn from data and improve over time without being explicitly programmed. Data Warehousing and ETL Processes What is a data warehouse, and why is it important?

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

IBM Journey to AI blog

JULY 3, 2024

Multiple data applications and formats make it harder for organizations to access, govern, manage and use all their data for AI effectively. Scaling data and AI with technology, people and processes Enabling data as a differentiator for AI requires a balance of technology, people and processes.

AI 45

AI AI Data Quality Database 45

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.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

AWS Machine Learning Blog

AUGUST 21, 2023

Our customers wanted the ability to connect to Amazon EMR to run ad hoc SQL queries on Hive or Presto to query data in the internal metastore or external metastore (such as the AWS Glue Data Catalog ), and prepare data within a few clicks.

AWS 98

AWS Data Lakes Clustering Data Preparation 98

phData

JULY 18, 2023

Slow Response to New Information: Legacy data systems often lack the computation power necessary to run efficiently and can be cost-inefficient to scale. This typically results in long-running ETL pipelines that cause decisions to be made on stale or old data. Modern data businesses need modern data governance.

Data Warehouse 52

Data Warehouse Analytics Analytics Cloud Data 52

Dataconomy

FEBRUARY 25, 2025

This guide offers a strategic pathway to implementing data systems that not only support current needs but are adaptable to future technological advancements. The evolution of artificial intelligence (AI) has highlighted the critical need for AI-ready data systems within modern enterprises.

Data Pipeline 103

Data Pipeline AI AI ETL 103