How to Learn Machine Learning

APRIL 26, 2025

Data Storage and Management Once data have been collected from the sources, they must be secured and made accessible. The responsibilities of this phase can be handled with traditional databases (MySQL, PostgreSQL), cloud storage (AWS S3, Google Cloud Storage), and big data frameworks (Hadoop, Apache Spark).

Data Science 52

Data Science Data Analyst Data Scientist Machine Learning 52

Tableau

DECEMBER 22, 2020

We recently wrapped up participation in the all-virtual AWS re:Invent 2020 where we shared our experiences from scaling Tableau Public ten-fold this year. Mary’s Bank holds regular Tableau office hours, is investing more time and energy in trainings, and has games that test employees on their Tableau knowledge. .

Analytics 52

Analytics Analytics Tableau AWS 52

phData

SEPTEMBER 15, 2023

For businesses utilizing Salesforce as their Customer Relationship Management (CRM) platform, the Snowflake Data Cloud and Tableau offer an excellent solution for scalable and accurate analytics. In order to unlock the potential of these tools, your CRM data must remain synced between Salesforce and Snowflake.

Data Warehouse 52

Data Warehouse Tableau Data Silos Analytics 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

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

Women in Big Data

DECEMBER 9, 2024

Through a comparative analysis of some of the leading BI tools: Google Looker, Microsoft Power BI, Tableau and Qlik Sense, discover which BI solution best fits your organization’s data analytics needs to empower informed decision-making. Selecting the right one can seem daunting.

Big Data 52

Big Data Big Data Power BI Tableau 52

ODSC - Open Data Science

FEBRUARY 6, 2024

This open-source streaming platform enables the handling of high-throughput data feeds, ensuring that data pipelines are efficient, reliable, and capable of handling massive volumes of data in real-time. Each platform offers unique features and benefits, making it vital for data engineers to understand their differences.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Alation

MARCH 1, 2022

Lineage helps them identify the source of bad data to fix the problem fast. Manual lineage will give ARC a fuller picture of how data was created between AWS S3 data lake, Snowflake cloud data warehouse and Tableau (and how it can be fixed). And connectivity is the crux of a powerful data catalog.

Data Warehouse 52

Data Warehouse Data Lakes Cloud Data Database 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. The current process involves analysts opening Excel files and copying and pasting values.

ETL 52

ETL Data Warehouse Data Governance Tableau 52

phData

MAY 25, 2023

One of the easiest ways for Snowflake to achieve this is to have analytics solutions query their data warehouse in real-time (also known as DirectQuery). Features like Power BI Premium Large Dataset Storage and Incremental Refresh should be considered for importing large data volumes.

Power BI 84

Power BI Analytics Analytics Azure 84

Pickl AI

JULY 25, 2023

Data Visualization: Matplotlib, Seaborn, Tableau, etc. Big Data Technologies: Hadoop, Spark, etc. Domain Knowledge: Understanding the specific domain where they apply data analysis. They work with databases and data warehouses to ensure data integrity and security. ETL Tools: Apache NiFi, Talend, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 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

phData

NOVEMBER 13, 2023

It’s about more than just looking at one project; dbt Explorer lets you see the lineage across different projects, ensuring you can track your data’s journey end-to-end without losing track of the details. Tableau (beta) Google Sheets (beta) Hex Klipfolio PowerMetrics Lightdash Mode Push.ai Source: Dave Connor's Loom.

Database 52

Database Business Intelligence Business Intelligence Data Silos 52

Data Science Blog

FEBRUARY 7, 2023

Vor einen Jahrzehnt war es immer noch recht üblich, sich einfach ein BI Tool zu nehmen, sowas wie QlikView, Tableau oder PowerBI, mittlerweile gibt es ja noch einige mehr, und da direkt die Daten reinzuladen und dann halt loszulegen mit dem Aufbau der Reports. Ein Data Warehouse ist eine oder eine Menge von Datenbanken.

Business Intelligence 286

Business Intelligence Business Intelligence Data Warehouse Data Lakes 286

Pickl AI

FEBRUARY 23, 2025

Statistics : A survey by Databricks revealed that 80% of Spark users reported improved performance in their data processing tasks compared to traditional systems. Google Cloud BigQuery Google Cloud BigQuery is a fully-managed enterprise data warehouse that enables super-fast SQL queries using the processing power of Googles infrastructure.

Big Data 52

Big Data Big Data Apache Hadoop Apache Kafka 52

Pickl AI

MARCH 19, 2025

It helps data engineers collect, store, and process streams of records in a fault-tolerant way, making it crucial for building reliable data pipelines. Amazon Redshift Amazon Redshift is a cloud-based data warehouse that enables fast query execution for large datasets.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52