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. Before we address the questions, ‘ What is data version control ?’

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Pickl AI

NOVEMBER 15, 2023

Discover the nuanced dissimilarities between Data Lakes and Data Warehouses. Data management in the digital age has become a crucial aspect of businesses, and two prominent concepts in this realm are Data Lakes and Data Warehouses. It acts as a repository for storing all the data.

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Analytics Vidhya

OCTOBER 25, 2023

Introduction Struggling with expanding a business database due to storage, management, and data accessibility issues? To steer growth, employ effective data management strategies and tools. This article explores data management’s key tool features and lists the top tools for 2023.

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IBM Journey to AI blog

MARCH 14, 2024

The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage. Also, traditional database management tasks, including backups, upgrades and routine maintenance drain valuable time and resources, hindering innovation.

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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 […]. Each business often uses one or more data […].

Data Warehouse 52

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Dataconomy

FEBRUARY 12, 2024

The acronym ETL—Extract, Transform, Load—has long been the linchpin of modern data management, orchestrating the movement and manipulation of data across systems and databases. This methodology has been pivotal in data warehousing, setting the stage for analysis and informed decision-making. Image credit ) 5.

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Tableau

MAY 11, 2022

Certified data sources carefully chosen by site administrators and project leaders. Recommended data sources personally certified and/or automatically selected based on organizational usage patterns. Recommended database tables that are used frequently in data sources and workbooks published to your Tableau server.

Tableau 98

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Tableau

MAY 11, 2022

Certified data sources carefully chosen by site administrators and project leaders. Recommended data sources personally certified and/or automatically selected based on organizational usage patterns. Recommended database tables that are used frequently in data sources and workbooks published to your Tableau server.

Tableau 98

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phData

NOVEMBER 9, 2023

What Components Make up the Snowflake Data Cloud? The main goal of a data mesh structure is to drive: Domain-driven ownership Data as a product Self-service infrastructure Federated governance One of the primary challenges that organizations face is data governance. What is a Cloud Data Warehouse?

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IBM Journey to AI blog

APRIL 25, 2023

To do so, Presto and Spark need to readily work with existing and modern data warehouse infrastructures. Now, let’s chat about why data warehouse optimization is a key value of a data lakehouse strategy. To effectively use raw data, it often needs to be curated within a data warehouse.

Data Warehouse 58

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Alation

APRIL 6, 2023

Data governance is traditionally applied to structured data assets that are most often found in databases and information systems. For one, spreadsheets are convenient and a low-cost, user-friendly alternative to larger databases and information systems.

Data Governance 52

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IBM Journey to AI blog

JULY 24, 2024

By using open formats, these solutions provide unified data access, allowing seamless sharing of data across an organization without the need for extensive migration or restructuring. The integration with established data warehouse engines ensures compatibility with existing systems and workflows.

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Precisely

JULY 12, 2024

However, simply having high-quality data does not, of itself, ensure that an organization will find it useful. That is where data integrity comes into play. Data quality : Data must be complete, unique, valid, timely, and consistent in order to be useful for decision making.

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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. Define data ownership, access controls, and data management processes to maintain the integrity and confidentiality of your data.

Analytics 203

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IBM Journey to AI blog

MAY 9, 2023

Watsonx.data will allow users to access their data through a single point of entry and run multiple fit-for-purpose query engines across IT environments. Through workload optimization an organization can reduce data warehouse costs by up to 50 percent by augmenting with this solution. [1]

Data Warehouse 92

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Pickl AI

OCTOBER 17, 2024

Introduction ETL plays a crucial role in Data Management. This process enables organisations to gather data from various sources, transform it into a usable format, and load it into data warehouses or databases for analysis.

ETL 52

ETL Data Warehouse Data Quality Data Governance 52

IBM Journey to AI blog

OCTOBER 30, 2023

Overall, this partnership enables the retailer to make data-driven decisions, improve supply chain efficiency and ultimately boost customer satisfaction, all in a secure and scalable cloud environment. The platform provides an intelligent, self-service data ecosystem that enhances data governance, quality and usability.

AWS 95

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ODSC - Open Data Science

JUNE 12, 2023

They all agree that a Datamart is a subject-oriented subset of a data warehouse focusing on a particular business unit, department, subject area, or business functionality. The Datamart’s data is usually stored in databases containing a moving frame required for data analysis, not the full history of data.

Power BI 52

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phData

APRIL 20, 2023

In today’s world, data-driven applications demand more flexibility, scalability, and auditability, which traditional data warehouses and modeling approaches lack. This is where the Snowflake Data Cloud and data vault modeling comes in handy. What is Data Vault Modeling?

Data Warehouse 72

Data Warehouse Data Governance Clustering Database 72

Pickl AI

NOVEMBER 4, 2024

Key Takeaways Data Engineering is vital for transforming raw data into actionable insights. Key components include data modelling, warehousing, pipelines, and integration. Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering?

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Precisely

JANUARY 25, 2024

There are three potential approaches to mainframe modernization: Data Replication creates a duplicate copy of mainframe data in a cloud data warehouse or data lake, enabling high-performance analytics virtually in real time, without negatively impacting mainframe performance. Best Practice 5.

Data Governance 69

Data Governance Database Cloud Data Data Lakes 69

phData

SEPTEMBER 19, 2023

A data lake is a centralized repository containing extensive storage for raw, unfiltered data coming into a company’s data storage system. This data can be structured, semi-structured, or unstructured and comes from various sources such as databases, IoT devices, log files, etc.

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Mlearning.ai

FEBRUARY 16, 2023

The ultimate need for vast storage spaces manifests in data warehouses: specialized systems that aggregate data coming from numerous sources for centralized management and consistency. In this article, you’ll discover what a Snowflake data warehouse is, its pros and cons, and how to employ it efficiently.

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Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. 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.

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Alation

APRIL 19, 2022

Data producers and consumers alike are working from home and hybrid locations more often. And in an increasingly remote workforce, people need to access data systems easily to do their jobs. This might mean that they’re accessing a database from a smartphone, computer, or tablet. Today, data dwells everywhere.

Data Governance 52

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Alation

JUNE 9, 2021

In this four-part blog series on data culture, we’re exploring what a data culture is and the benefits of building one, and then drilling down to explore each of the three pillars of data culture – data search & discovery, data literacy, and data governance – in more depth.

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Alation

JULY 22, 2021

Our platform combines data insights with human intelligence in pursuit of this mission. Susannah Barnes, an Alation customer and senior data governance specialist at American Family Insurance, introduced our team to faculty at the School of Information Studies of the University of Wisconsin, Milwaukee (UWM-SOIS), her alma mater.

Big Data 98

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Dataconomy

FEBRUARY 23, 2023

Collecting, storing, and processing large datasets Data engineers are also responsible for collecting, storing, and processing large volumes of data. This involves working with various data storage technologies, such as databases and data warehouses, and ensuring that the data is easily accessible and can be analyzed efficiently.

Big Data 91

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Alation

FEBRUARY 13, 2020

Data curation is important in today’s world of data sharing and self-service analytics, but I think it is a frequently misused term. When speaking and consulting, I often hear people refer to data in their data lakes and data warehouses as curated data, believing that it is curated because it is stored as shareable data.

Data Warehouse 52

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AWS Machine Learning Blog

JUNE 5, 2023

One of the most common formats for storing large amounts of data is Apache Parquet due to its compact and highly efficient format. This means that business analysts who want to extract insights from the large volumes of data in their data warehouse must frequently use data stored in Parquet. Choose Grant.

Machine Learning 95

Machine Learning Machine Learning AWS Data Lakes 95

Pickl AI

OCTOBER 19, 2023

Implementing validation rules helps prevent incorrect or incomplete data from being added to your databases. Regular Data Audits Conduct regular data audits to identify issues and discrepancies. This proactive approach allows you to detect and address problems before they compromise data quality.

Data Quality 52

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Precisely

JULY 18, 2024

While data fabric is not a standalone solution, critical capabilities that you can address today to prepare for a data fabric include automated data integration, metadata management, centralized data governance, and self-service access by consumers. Increase metadata maturity.

Data Lakes 52

Data Lakes Data Warehouse Data Governance Machine Learning 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.

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phData

APRIL 29, 2024

Understanding Fivetran Fivetran is a popular Software-as-a-Service platform that enables users to automate the movement of data and ETL processes across diverse sources to a target destination. The phData team achieved a major milestone by successfully setting up a secure end-to-end data pipeline for a substantial healthcare enterprise.

SQL 52

SQL Data Warehouse Azure Cloud Data 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

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IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Perform data quality monitoring based on pre-configured rules.

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

Alation

APRIL 14, 2022

Semantics, context, and how data is tracked and used mean even more as you stretch to reach post-migration goals. This is why, when data moves, it’s imperative for organizations to prioritize data discovery. Data discovery is also critical for data governance , which, when ineffective, can actually hinder organizational growth.

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