Pickl AI

DECEMBER 16, 2024

Summary : Microsoft Fabric is an end-to-end Data Analytics platform designed for integration, processing, and advanced insights, while Power BI excels in creating interactive visualisations and reports. Key Takeaways Microsoft Fabric is a full-scale data platform, while Power BI focuses on visualising insights.

Power BI 52

Power BI Analytics Analytics Machine Learning 52

Pickl AI

MARCH 10, 2025

Summary: Power BI is a business intelligence tool that transforms raw data into actionable insights. Power BI enhances decision-making by providing interactive dashboards and reports that are accessible to both technical and non-technical users. What Is Power BI?

Power BI 52

Power BI Business Intelligence Business Intelligence Analytics 52

ODSC - Open Data Science

APRIL 6, 2023

Though both are great to learn, what gets left out of the conversation is a simple yet powerful programming language that everyone in the data science world can agree on, SQL. But why is SQL, or Structured Query Language , so important to learn? Let’s start with the first clause often learned by new SQL users, the WHERE clause.

SQL 98

SQL Data Scientist Database Data Science 98

phData

JULY 11, 2023

Power BI Datamarts is one of the most exciting features that Microsoft has released for the Power Platform in recent years. If you need high-level information on what a Power BI Datamart is and some example use cases, check out our other blog, What Are Power BI Datamarts?

Power BI 40

Power BI SQL Azure ETL 40

How to Learn Machine Learning

APRIL 26, 2025

The processes of SQL, Python scripts, and web scraping libraries such as BeautifulSoup or Scrapy are used for carrying out the data collection. Tools like Python (with pandas and NumPy), R, and ETL platforms like Apache NiFi or Talend are used for data preparation before analysis.

Data Science 52

Data Science Data Analyst Data Scientist Machine Learning 52

Towards AI

APRIL 2, 2024

Steps Involved in the Data Analysis Process (General Steps to Follow as a Data Analyst) Gather the data in any spreadsheet or database and gain a basic understanding of the data that you have using any statistical techniques or by writing SQL Queries. We have many tools that offer features like ETL, Visualization, and validations.

Data Analysis 94

Data Analysis Data Analysis Data Analyst Power BI 94

Pickl AI

MARCH 19, 2025

Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Python, SQL, and Apache Spark are essential for data engineering workflows. SQL Structured Query Language ( SQL ) is a fundamental skill for data engineers.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Data Science Dojo

JULY 3, 2024

Tools like Tableau, Power BI, and Python libraries such as Matplotlib and Seaborn are commonly taught. Databases and SQL : Managing and querying relational databases using SQL, as well as working with NoSQL databases like MongoDB. R : Often used for statistical analysis and data visualization.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Dataconomy

JULY 28, 2023

Here are some of the best data preprocessing tools of 2023: Microsoft Power BI Tableau Trifacta Talend Toad Data Point Power Query Microsoft Power BI Microsoft Power BI is a comprehensive data preparation tool that allows users to create reports with multiple complex data sources.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Smart Data Collective

MAY 20, 2019

The popular tools, on the other hand, include Power BI, ETL, IBM Db2, and Teradata. SQL programming skills, specific tool experience — Tableau for example — and problem-solving are just a handful of examples. Professionals adept at this skill will be desirable by corporations, individuals and government offices alike.

Analytics 111

Analytics Analytics Data Analyst Machine Learning 111

Pickl AI

MARCH 3, 2025

Key skills include SQL, data visualization, and business acumen. This blog will comprehensively explore the world of BI, dissecting what it is, the multifaceted responsibilities of a BI Analyst, and how to embark on this rewarding career path. Essential skills include SQL, data visualization, and strong analytical abilities.

Business Intelligence 52

Business Intelligence Business Intelligence Data Analyst Data Visualization 52

ODSC - Open Data Science

APRIL 3, 2023

Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis. Competence in data quality, databases, and ETL (Extract, Transform, Load) are essential. SQL excels with big data and statistics, making it important in order to query databases.

Analytics 52

Analytics Analytics Data Analyst Data Science 52

Pickl AI

JULY 3, 2023

Here are steps you can follow to pursue a career as a BI Developer: Acquire a solid foundation in data and analytics: Start by building a strong understanding of data concepts, relational databases, SQL (Structured Query Language), and data modeling.

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Alation

JANUARY 17, 2023

Reverse ETL tools. Business intelligence (BI) platforms. The modern data stack is also the consequence of a shift in analysis workflow, fromextract, transform, load (ETL) to extract, load, transform (ELT). A Note on the Shift from ETL to ELT. In the past, data movement was defined by ETL: extract, transform, and load.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

Dataconomy

MARCH 24, 2023

BI developer:  A BI developer is responsible for designing and implementing BI solutions, including data warehouses, ETL processes, and reports. Database management:  A BI professional should be able to design and manage databases, including data modeling, ETL processes, and data integration.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Dataconomy

MARCH 24, 2023

BI developer:  A BI developer is responsible for designing and implementing BI solutions, including data warehouses, ETL processes, and reports. Database management:  A BI professional should be able to design and manage databases, including data modeling, ETL processes, and data integration.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

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. What Are Some Common Tools Used in Business Intelligence Architecture?

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

Pickl AI

APRIL 26, 2024

It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques. Key Takeaways SQL Mastery: Understand SQL’s importance, join tables, and distinguish between SELECT and SELECT DISTINCT. How do you join tables in SQL?

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Pickl AI

AUGUST 9, 2024

Data Modelling Tools Tools such as ER/Studio, Oracle SQL Developer Data Modeler, and IBM InfoSphere Data Architect allow users to design and visualise hierarchies within dimensional models.

Data Warehouse 52

Data Warehouse Data Quality ETL Business Intelligence 52

DataSeries

AUGUST 15, 2024

Knowledge of Core Data Engineering Concepts Ensure one possess a strong foundation in core data engineering concepts, which include data structures, algorithms, database management systems, data modeling , data warehousing , ETL (Extract, Transform, Load) processes, and distributed computing frameworks (e.g., Hadoop, Spark).

Data Engineering 40

Data Engineering Data Engineering Data Engineer Data Engineering 40

Pickl AI

AUGUST 11, 2024

ETL Tools Informatica, Talend, and Apache Airflow enable the extraction of data from source systems, transformation into the desired format, and loading into the dimensional model. These tools help streamline the design process and ensure consistency. These tools are essential for populating fact tables with accurate and timely data.

Data Quality 52

Data Quality Data Warehouse Data Governance Analytics 52

Pickl AI

AUGUST 9, 2024

Understanding the differences between SQL and NoSQL databases is crucial for students. Understanding ETL (Extract, Transform, Load) processes is vital for students. Visualisation Tools Familiarity with tools such as Tableau, Power BI, and D3.js js for creating interactive visualisations.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Data Science Blog

JULY 20, 2024

Automatisierung: Erstellt SQL-Code, DACPAC-Dateien, SSIS-Pakete, Data Factory-ARM-Vorlagen und XMLA-Dateien. Vielfältige Unterstützung: Kompatibel mit verschiedenen Datenbankmanagementsystemen wie MS SQL Server und Azure Synapse Analytics. Frontends : Kompatibel mit Tools wie Power BI, Qlik Sense und Tableau.

Azure 130

Azure Power BI SQL Data Lakes 130

Pickl AI

JULY 18, 2024

Relational databases use SQL for querying, which can be complex and rigid. Explain The Difference Between MongoDB and SQL Databases. MongoDB is a NoSQL database that stores data in documents, while SQL databases store data in tables with rows and columns. Documents are stored in collections, analogous to SQL database tables.

Database 52

Database SQL Data Analyst Database Administration 52

Flipboard

JUNE 16, 2025

SQL and MongoDB SQL remains critical for structured data management, while MongoDB caters to NoSQL database needs, which is essential for modern and flexible data applications. Data Analysis and Transition to Machine Learning: Skills: Python, SQL, Excel, Tableau and Power BI are relevant skills for entry-level data analysis roles.

Python 50

Python AWS Machine Learning Machine Learning 50