Precisely

MARCH 9, 2023

Data warehouse vs. data lake, each has their own unique advantages and disadvantages; it’s helpful to understand their similarities and differences. In this article, we’ll focus on a data lake vs. data warehouse. Read Many of the preferred platforms for analytics fall into one of these two categories.

Data Lakes 59

Data Lakes Data Warehouse Hadoop Apache Hadoop 59

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

Data Warehouse ETL Data Governance Data Quality 52

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. Schema Enforcement: Data warehouses use a “schema-on-write” approach.

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Data Lakes Data Warehouse Database Big Data 98

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. Choosing the right ETL tool is crucial for smooth data management.

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phData

SEPTEMBER 19, 2023

However, to fully harness the potential of a data lake, effective data modeling methodologies and processes are crucial. Data modeling plays a pivotal role in defining the structure, relationships, and semantics of data within a data lake. Consistency of data throughout the data lake.

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Data Lakes Data Modeling Data Models Data Warehouse 52

phData

OCTOBER 9, 2023

In this blog, we explore best practices and techniques to optimize Snowflake’s performance for data vault modeling , enabling your organizations to achieve efficient data processing, accelerated query performance, and streamlined ETL workflows.

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ETL Clustering Data Warehouse SQL 69

ODSC - Open Data Science

JUNE 12, 2023

This article is an excerpt from the book Expert Data Modeling with Power BI, Third Edition by Soheil Bakhshi, a completely updated and revised edition of the bestselling guide to Power BI and data modeling. in an enterprise data warehouse. What is a Datamart?

Power BI 52

Power BI Data Warehouse ETL Data Preparation 52

Dataconomy

SEPTEMBER 4, 2023

Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.

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Analytics Analytics Big Data Analytics Big Data Analytics 203

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.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

phData

JULY 17, 2023

Over the past few decades, the corporate data landscape has changed significantly. The shift from on-premise databases and spreadsheets to the modern era of cloud data warehouses and AI/ LLMs has transformed what businesses can do with data. This is where Fivetran and the Modern Data Stack come in.

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Data Warehouse Data Pipeline Cloud Data ETL 52

phData

SEPTEMBER 27, 2024

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

phData

JUNE 6, 2024

These traditional CDPs are designed to gather and house their own data store—separate from the core data infrastructure. Because of this separation, data models are rigid, and the setup process is costly and lengthy. Data gets ingested, centralized, and deployed within your cloud data warehouse.

Data Warehouse 52

Data Warehouse Cloud Data ETL Data Modeling 52

Pickl AI

JUNE 28, 2023

Data cleaning, normalization, and reformatting to match the target schema is used. · Data Loading It is the final step where transformed data is loaded into a target system, such as a data warehouse or a data lake. It ensures that the integrated data is available for analysis and reporting.

Data Mining 52

Data Mining Data Mining Data Mining ETL 52

Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

phData

SEPTEMBER 25, 2024

With the importance of data in various applications, there’s a need for effective solutions to organize, manage, and transfer data between systems with minimal complexity. While numerous ETL tools are available on the market, selecting the right one can be challenging. What is Fivetran?

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Pickl AI

AUGUST 9, 2024

Hierarchies align data modelling with business processes, making it easier to analyse data in a context that reflects real-world operations. Designing Hierarchies Designing effective hierarchies requires careful consideration of the business requirements and the data model.

Data Warehouse 52

Data Warehouse Data Quality ETL Business Intelligence 52

Pickl AI

JULY 3, 2023

It is the process of converting raw data into relevant and practical knowledge to help evaluate the performance of businesses, discover trends, and make well-informed choices. Data gathering, data integration, data modelling, analysis of information, and data visualization are all part of intelligence for businesses.

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

phData

SEPTEMBER 26, 2023

Data flows from the current data platform to the destination. Transformations Transformations can be a part of data ingestion (ETL pattern) or can take place at a later stage after data has been landed (ELT pattern). Either way, it’s important to understand what data is transformed, and how so.

SQL 52

SQL Database ETL Data Modeling 52

phData

MAY 11, 2023

With the “Data Productivity Cloud” launch, Matillion has achieved a balance of simplifying source control, collaboration, and dataops by elevating Git integration to a “first-class citizen” within the framework. In Matillion ETL, the Git integration enables an organization to connect to any Git offering (e.g.,

ETL 52

ETL Analytics Analytics Data Modeling 52

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 Business Intelligence 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 Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Pickl AI

AUGUST 11, 2024

Retail Sales In a retail data warehouse , the sales fact table might include metrics such as sales revenue, units sold, discounts applied, and profit margins. Web Analytics In a web analytics data warehouse, the page views fact table might include metrics such as total page views, unique visitors, session duration, and bounce rate.

Data Quality 52

Data Quality Data Warehouse Data Governance Business Intelligence 52

phData

JULY 10, 2024

Unlike traditional BI tools, its user-friendly interface ensures that users of all technical levels can seamlessly interact with data. The platform’s integration with cloud data warehouses like Snowflake AI Data Cloud , Google BigQuery, and Amazon Redshift makes it a vital tool for organizations harnessing big data.

SQL 52

SQL Data Warehouse Data Analysis Data Analysis 52

phData

JUNE 27, 2023

Data Ingestion with Fivetran Fivetran is used to move your source(s) into a centralized space for storage. Data Storage with Snowflake Snowflake is the main data warehouse, the foundation. Storing all the collected data sent from Fivetran Once in Snowflake, the data is ready to be accessed and analyzed.

SQL 52

SQL Database Data Quality Data Warehouse 52

Alation

OCTOBER 27, 2022

Few actors in the modern data stack have inspired the enthusiasm and fervent support as dbt. This data transformation tool enables data analysts and engineers to transform, test and document data in the cloud data warehouse. Jason: How do you use these models?

Data Analyst 52

Data Analyst Data Scientist Analytics Analytics 52

The MLOps Blog

MARCH 15, 2023

If you will ask data professionals about what is the most challenging part of their day to day work, you will likely discover their concerns around managing different aspects of data before they get to graduate to the data modeling stage. Pricing It is free to use and is licensed under Apache License Version 2.0.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

phData

OCTOBER 17, 2024

Apache Airflow Airflow is an open-source ETL software that is very useful when paired with Snowflake. By leveraging version control, testing, and documentation features, dbt Core enables teams to ensure data quality and consistency across their pipelines while integrating seamlessly with modern data warehouses.

AI 52

AI AI SQL Data Quality 52

Pickl AI

APRIL 6, 2023

Understand the fundamentals of data engineering: To become an Azure Data Engineer, you must first understand the concepts and principles of data engineering. Knowledge of data modeling, warehousing, integration, pipelines, and transformation is required. For Azure Data Engineer, there are various skills required.

Azure 52

Azure Data Engineer Data Engineering Data Engineering 52

phData

AUGUST 10, 2023

Data Vault - Data Lifecycle Architecturally, let’s understand the data lifecycle in the data vault into the following layers, which play a key role in choosing the right pattern and tools to implement. Data Acquisition: Extracting data from source systems and making it accessible.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

DagsHub

NOVEMBER 11, 2024

In this article, we’ll explore how AI can transform unstructured data into actionable intelligence, empowering you to make informed decisions, enhance customer experiences, and stay ahead of the competition. What is Unstructured Data? Word2Vec , GloVe , and BERT are good sources of embedding generation for textual data.

AI 52

AI AI Data Lakes Database 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.

Data Warehouse 52

Data Warehouse ETL Data Mining Data Mining 52

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

phData

JULY 18, 2023

With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. This typically results in long-running ETL pipelines that cause decisions to be made on stale or old data.

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Data Warehouse Analytics Analytics SQL 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

An example direct acyclic graph (DAG) might automate data ingestion, processing, model training, and deployment tasks, ensuring that each step is run in the correct order and at the right time. Though it’s worth mentioning that Airflow isn’t used at runtime as is usual for extract, transform, and load (ETL) tasks.

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