Dataconomy

MARCH 4, 2025

Each stage is crucial for deriving meaningful insights from data. Data gathering The first step is gathering relevant data from various sources. This could include data warehouses, data lakes, or even external datasets.

Data Mining 91

Data Mining Data Mining Data Mining Decision Trees 91

ODSC - Open Data Science

FEBRUARY 24, 2023

While machine learning frameworks and platforms like PyTorch, TensorFlow, and scikit-learn can perform data exploration well, it’s not their primary intent. There are also plenty of data visualization libraries available that can handle exploration like Plotly, matplotlib, D3, Apache ECharts, Bokeh, etc.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

Dataconomy

SEPTEMBER 4, 2023

Here are some of the key types of cloud analytics: Descriptive analytics: This type focuses on summarizing historical data to provide insights into what has happened in the past. It helps organizations understand trends, patterns, and anomalies in their data. Ensure that data is clean, consistent, and up-to-date.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

Smart Data Collective

DECEMBER 13, 2022

Extracted data must be saved someplace. There are several choices to consider, each with its own set of advantages and disadvantages: Data warehouses are used to store data that has been processed for a specific function from one or more sources.

Analytics 145

Analytics Analytics Data Analysis Data Analysis 145

Women in Big Data

OCTOBER 9, 2024

The success of any data initiative hinges on the robustness and flexibility of its big data pipeline. What is a Data Pipeline? 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.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

Tableau

DECEMBER 7, 2022

Traditionally, organizations built complex data pipelines to replicate data. Those data architectures were brittle, complex, and time intensive to build and maintain, requiring data duplication and bloated data warehouse investments. Cut costs by consolidating data warehouse investments.

Tableau 98

Tableau Data Warehouse Data Pipeline Data Visualization 98

Tableau

DECEMBER 7, 2022

Traditionally, organizations built complex data pipelines to replicate data. Those data architectures were brittle, complex, and time intensive to build and maintain, requiring data duplication and bloated data warehouse investments. Cut costs by consolidating data warehouse investments.

Tableau 98

Tableau Data Warehouse Data Pipeline Data Visualization 98

IBM Journey to AI blog

SEPTEMBER 19, 2023

js and Tableau Data science, data analytics and IBM Practicing data science isn’t without its challenges. There can be fragmented data, a short supply of data science skills and rigid IT standards for training and deployment. Watsonx comprises of three powerful components: the watsonx.ai

Data Science 94

Data Science Analytics Analytics Data Scientist 94

Alation

MAY 16, 2023

Data Pipeline Use Cases Here are just a few examples of the goals you can achieve with a robust data pipeline: Data Prep for Visualization Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state.

Data Pipeline 69

Data Pipeline Data Governance Data Lakes Data Warehouse 69

Pickl AI

JANUARY 28, 2025

This involves several key processes: Extract, Transform, Load (ETL): The ETL process extracts data from different sources, transforms it into a suitable format by cleaning and enriching it, and then loads it into a data warehouse or data lake. Data Lakes: These store raw, unprocessed data in its original format.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

Tableau

DECEMBER 7, 2022

Traditionally, organizations built complex data pipelines to replicate data. Those data architectures were brittle, complex, and time intensive to build and maintain, requiring data duplication and bloated data warehouse investments. Salesforce Data Cloud for Tableau solves those challenges.

Tableau 55

Tableau Data Warehouse Data Pipeline Data Visualization 55

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.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

IBM Journey to AI blog

JULY 3, 2024

This includes integration with your data warehouse engines, which now must balance real-time data processing and decision-making with cost-effective object storage, open source technologies and a shared metadata layer to share data seamlessly with your data lakehouse.

Data Quality 45

Data Quality AI AI Database 45

Alation

MAY 16, 2023

Data Pipeline Use Cases Here are just a few examples of the goals you can achieve with a robust data pipeline: Data Prep for Visualization Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state.

Data Pipeline 40

Data Pipeline Data Governance Data Lakes Data Warehouse 40

IBM Journey to AI blog

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization.

Data Quality 52

Data Quality Data Governance Data Wrangling Data Scientist 52

Alation

FEBRUARY 13, 2020

Data Literacy—Many line-of-business people have responsibilities that depend on data analysis but have not been trained to work with data. Their tendency is to do just enough data work to get by, and to do that work primarily in Excel spreadsheets. Who needs data literacy training? Who can provide the training?

Data Governance 52

Data Governance Data Analysis Data Analysis Data Preparation 52

Dataversity

APRIL 1, 2021

In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […].

Analytics 69

Analytics Analytics Data Silos Data Lakes 69

Pickl AI

SEPTEMBER 5, 2024

Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.

Azure 52

Azure Data Scientist Data Science Machine Learning 52