Women in Big Data

OCTOBER 9, 2024

A traditional data pipeline is a structured process that begins with gathering data from various sources and loading it into a data warehouse or data lake. Once ingested, the data is prepared through filtering, error correction, and restructuring for ease of use.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

How to Learn Machine Learning

APRIL 26, 2025

Data Cleaning and Preparation The tasks of cleaning and preparing the data take place before the analysis. This includes duplicate removal, missing value treatment, variable transformation, and normalization of data. Deployment and Monitoring Once a model is built, it is moved to production.

Data Science 52

Data Science Data Analyst Data Scientist Machine Learning 52

phData

AUGUST 9, 2024

Typically, this data is scattered across Excel files on business users’ desktops. It is extremely labor intensive, and the team wants to automate it using Snowflake and Tableau. Financial data is pulled from the ERP. Snowflake can not natively read files on these services, so an ETL service is needed to upload the data.

ETL 52

ETL Data Warehouse Data Governance Tableau 52

Pickl AI

JANUARY 15, 2025

They help stakeholders monitor performance at a glance and make timely decisions based on current data. ETL (Extract, Transform, Load) Tools ETL tools are crucial for data integration processes. It allows users to create interactive dashboards and share insights across the organization seamlessly.

Business Intelligence 52

Business Intelligence Business Intelligence Power BI Data Visualization 52

Pickl AI

JANUARY 28, 2025

Data Integration Once data is collected from various sources, it needs to be integrated into a cohesive format. Data Quality Management : Ensures that the integrated data is accurate, consistent, and reliable for analysis. This can involve: Data Warehouses: These are optimized for query performance and reporting.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

Pickl AI

MARCH 3, 2025

However, some core responsibilities include Data Warehousing and Management Designing and maintaining data warehouses and data marts to support Data Analysis and reporting. Ensuring data integrity and security. Gain proficiency in data visualization tools like Tableau, Power BI, or Looker.

Business Intelligence 52

Business Intelligence Business Intelligence Data Analyst Data Visualization 52

phData

FEBRUARY 14, 2023

Data integration is essentially the Extract and Load portion of the Extract, Load, and Transform (ELT) process. Data ingestion involves connecting your data sources, including databases, flat files, streaming data, etc, to your data warehouse. Snowflake provides native ways for data ingestion.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

Dataconomy

MARCH 24, 2023

They may also be involved in data modeling and database design. BI developer:  A BI developer is responsible for designing and implementing BI solutions, including data warehouses, ETL processes, and reports. They may also be involved in data integration and data quality assurance.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Dataconomy

MARCH 24, 2023

They may also be involved in data modeling and database design. BI developer:  A BI developer is responsible for designing and implementing BI solutions, including data warehouses, ETL processes, and reports. They may also be involved in data integration and data quality assurance.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

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 Visualization: Matplotlib, Seaborn, Tableau, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

AUGUST 9, 2024

Real-world Examples To illustrate the practical applications of hierarchies in dimensional modelling, this section explores real-world examples across various industries, showcasing how hierarchies enhance data organisation, analysis, and decision-making.

Data Warehouse 52

Data Warehouse Data Quality ETL Business Intelligence 52

Pickl AI

JULY 3, 2023

Familiarize yourself with data analysis techniques and tools. Learn BI technologies: Gain proficiency in popular BI tools and technologies such as Microsoft Power BI, Tableau, QlikView, or MicroStrategy. BI Developer Skills Required To excel in this role, BI Developers need to possess a range of technical and soft skills.

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

Alation

FEBRUARY 20, 2020

Data lakes, while useful in helping you to capture all of your data, are only the first step in extracting the value of that data.

Data Lakes 40

Data Lakes ETL Data Analyst Data Preparation 40

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

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 Analytics 52

Pickl AI

AUGUST 9, 2024

Data Warehousing Solutions Tools like Amazon Redshift, Google BigQuery, and Snowflake enable organisations to store and analyse large volumes of data efficiently. Students should learn about the architecture of data warehouses and how they differ from traditional databases. js for creating interactive visualisations.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

phData

FEBRUARY 5, 2024

There are many different third-party tools that work with Snowflake: Fivetran Fivetran is a tool dedicated to replicating applications, databases, events, and files into a high-performance data warehouse, such as Snowflake.

SQL 52

SQL Database Data Quality Data Warehouse 52

Alation

JANUARY 25, 2022

This will allow them to be certain it’s the data they need, at which point they can use their data prep or pipeline tools against the actual source to continue with the development phase of their work. Alation partners such as Dataiku, Trifacta, and Tableau are perfect examples.

DataOps 52

DataOps SQL ML ML 52

Data Science Blog

JULY 20, 2024

Es bietet vollständige Automatisierung des BI-Stacks und unterstützt ein breites Spektrum an Data Warehouses, analytischen Datenbanken und Frontends. Automatisierung: Erstellt SQL-Code, DACPAC-Dateien, SSIS-Pakete, Data Factory-ARM-Vorlagen und XMLA-Dateien. Data Lakes: Unterstützt MS Azure Blob Storage.

Azure 130

Azure SQL Power BI Data Lakes 130