phData

JUNE 23, 2023

Data lineage provides a detailed understanding of how data is generated, captured, modified, and utilized. 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.

Data Quality 52

Data Quality Data Governance ETL Database 52

Dataversity

NOVEMBER 15, 2023

Those who want to design universal data pipelines and ETL testing tools face a tough challenge because of the vastness and variety of technologies: Each data pipeline platform embodies a unique philosophy, architectural design, and set of operations.

Data Pipeline 59

Data Pipeline ETL Data Governance Data Quality 59

Precisely

MAY 31, 2023

When attempting to build a data strategy, the primary obstacle organizations face is a lack of resources. Teams are building complex, hybrid, multi-cloud environments, moving critical data workloads to the cloud, and addressing data quality challenges. In many cases, data arrived too late to be useful.

Data Quality 52

Data Quality Data Governance Data Observability ETL 52

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.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

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 75

Database Data Engineering Data Engineer Data Engineering 75

Dataversity

DECEMBER 14, 2021

Watching closely the evolution of metadata platforms (later rechristened as Data Governance platforms due to their focus), as somebody who has implemented and built Data Governance solutions on top of these platforms, I see a significant evolution in their architecture as well as the use cases they support.

Data Governance 52

Data Governance Data Observability ETL Data Quality 52

Precisely

JUNE 6, 2023

Salam noted that organizations are offloading computational horsepower and data from on-premises infrastructure to the cloud. This provides developers, engineers, data scientists and leaders with the opportunity to more easily experiment with new data practices such as zero-ETL or technologies like AI/ML.

Data Observability 52

Data Observability Data Lakes Data Quality ETL 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

Data Science Dojo

SEPTEMBER 6, 2023

Once authenticated, authorization ensures that the individual is allowed access only to the areas they are authorized to enter. Data Governance: Setting the Rules D ata governance takes on the role of a regulatory framework, guiding the responsible management, utilization, and protection of your organization’s most valuable asset—data.

Data Warehouse 195

Data Warehouse Data Governance ETL Data Quality 195

The MLOps Blog

MARCH 15, 2023

Data governance: Ensure that the data used to train and test the model, as well as any new data used for prediction, is properly governed. For small-scale/low-value deployments, there might not be many items to focus on, but as the scale and reach of deployment go up, data governance becomes crucial.

AWS 52

AWS ETL ML ML 52

Pickl AI

APRIL 26, 2024

Data Warehousing and ETL Processes What is a data warehouse, and why is it important? A data warehouse is a centralised repository that consolidates data from various sources for reporting and analysis. It is essential to provide a unified data view and enable business intelligence and analytics.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 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

ODSC - Open Data Science

JUNE 19, 2023

In particular, its progress depends on the availability of related technologies that make the handling of huge volumes of data possible. These technologies include the following: Data governance and management  — It is crucial to have a solid data management system and governance practices to ensure data accuracy, consistency, and security.

Big Data 96

Big Data Big Data Artificial Intelligence Artificial Intelligence 96

phData

SEPTEMBER 19, 2023

Cost reduction by minimizing data redundancy, improving data storage efficiency, and reducing the risk of errors and data-related issues. Data Governance and Security By defining data models, organizations can establish policies, access controls, and security measures to protect sensitive data.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

Precisely

MARCH 9, 2023

Raw Data Data warehouses emerged several decades ago as a means of combining, harmonizing, and preprocessing data in preparation for advanced analytics. A data warehouse implies a certain degree of preprocessing, or at the very least, an organized and well-defined data model.

Data Lakes 59

Data Lakes Data Warehouse Hadoop Apache Hadoop 59

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 is critically dependent on robust governance and cataloging capabilities.

DataOps 52

DataOps Data Pipeline Data Engineering Data Engineer 52

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.

Data Engineering 91

Data Engineering Data Engineer Data Engineering Data Engineering 91

phData

AUGUST 17, 2023

They offer a range of features and integrations, so the choice depends on factors like the complexity of your data pipeline, requirements for connections to other services, user interface, and compatibility with any ETL software already in use. This enhances the reliability and resilience of the data pipeline.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

NOVEMBER 15, 2023

What Is a Data Warehouse? On the other hand, a Data Warehouse is a structured storage system designed for efficient querying and analysis. It involves the extraction, transformation, and loading (ETL) process to organize data for business intelligence purposes. It often serves as a source for Data Warehouses.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

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.

Data Pipeline 69

Data Pipeline Data Governance Data Lakes Data Warehouse 69

phData

AUGUST 10, 2023

In data vault implementations, critical components encompass the storage layer, ELT technology, integration platforms, data observability tools, Business Intelligence and Analytics tools, Data Governance , and Metadata Management solutions. This can create data quality challenges if not addressed properly.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

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 84

AWS Database AI AI 84

Dataversity

JUNE 7, 2022

Unlocking value from data is a journey. It involves investing in data infrastructure, analysts, scientists, and processes for managing data consumption. Even when data operations teams progress along this journey, growing pains crop up as more users want more data. You don’t have to grin […].

ETL 52

ETL Data Governance Data Quality Cloud Data 52

Dataversity

FEBRUARY 1, 2021

In Part 1 and Part 2 of this series, we described how data warehousing (DW) and business intelligence (BI) projects are a high priority for many organizations. Project sponsors seek to empower more and better data-driven decisions and actions throughout their enterprise; they intend to expand their […].

Data Warehouse 59

Data Warehouse Business Intelligence Business Intelligence Data Profiling 59

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.

Data Pipeline 40

Data Pipeline Data Governance Data Lakes Data Warehouse 40

Dataversity

MARCH 1, 2021

Data warehouse (DW) testers with data integration QA skills are in demand. Data warehouse disciplines and architectures are well established and often discussed in the press, books, and conferences. Each business often uses one or more data […]. Click to learn more about author Wayne Yaddow.

Data Warehouse 52

Data Warehouse ETL Data Governance Data Quality 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. Let’s look at some example transforms.

AWS 83

AWS Data Lakes Clustering Data Preparation 83

Alation

JANUARY 25, 2022

This is a key component of active data governance. These capabilities are also key for a robust data fabric. Another key nuance of a data fabric is that it captures social metadata. Social metadata captures the associations that people create with the data they produce and consume. (See

DataOps 52

DataOps SQL ML ML 52

Alation

OCTOBER 19, 2021

This can make collaboration across departments difficult, leading to inconsistent data quality , a lack of communication and visibility, and higher costs over time (among other issues). Using these solutions helps break down barriers between teams, allowing them to create a comprehensive data catalog.

Data Silos 52

Data Silos ETL Data Governance Data Quality 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 65

Data Lakes AI AI Data Governance 65

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 SQL Cloud Data Analytics 52

Dataversity

AUGUST 5, 2022

Creating a collaborative, data-driven culture is one of the most important goals of many modern organizations. However, access to the data and data processing tools remains restricted to the selected few technical users or upper management echelons. Data-driven culture cannot exist without the democratization of the data.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52