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

ODSC - Open Data Science

APRIL 6, 2023

It’s a foundational skill for working with relational databases Just about every data scientist or analyst will have to work with relational databases in their careers. Another boon for efficient work that SQL provides is its simple and consistent syntax that allows for collaboration across multiple databases.

SQL 98

SQL Data Scientist Database Data Science 98

Alation

JANUARY 17, 2023

Reverse ETL tools. The modern data stack is also the consequence of a shift in analysis workflow, fromextract, transform, load (ETL) to extract, load, transform (ELT). Later, BI tools such as Chartio, Looker, and Tableau arrived on the data scene. A Note on the Shift from ETL to ELT. Extract, load, Transform (ELT) tools.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

Pickl AI

JANUARY 15, 2025

The primary functions of BI tools include: Data Collection: Gathering data from multiple sources including internal databases, external APIs, and cloud services. They employ techniques from statistics, Machine Learning, and database systems to reveal insights that can inform strategic decisions.

Business Intelligence 52

Business Intelligence Business Intelligence Power BI Data Visualization 52

Pickl AI

MARCH 3, 2025

It gathers information from various sources sales databases, marketing platforms, customer feedback, and more and consolidates it into a unified view. Here’s a glimpse into their typical activities Data Acquisition and Cleansing Collecting data from diverse sources, including databases, spreadsheets, and cloud platforms.

Business Intelligence 52

Business Intelligence Business Intelligence Data Analyst Data Visualization 52

Smart Data Collective

MAY 20, 2019

With databases, for example, choices may include NoSQL, HBase and MongoDB but its likely priorities may shift over time. 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.

Analytics 111

Analytics Analytics Data Analyst Machine Learning 111

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. Proficiency in SQL Server, Oracle, or MySQL is often required.

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. Their primary responsibilities include: Data Collection and Preparation Data Scientists start by gathering relevant data from various sources, including databases, APIs, and online platforms. ETL Tools: Apache NiFi, Talend, etc.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

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

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

Pickl AI

JANUARY 28, 2025

They encompass all the origins from which data is collected, including: Internal Data Sources: These include databases, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and flat files within an organization. databases), semi-structured (e.g., Data can be structured (e.g.,

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 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. As you see, there are a number of reporting platforms as expected.

Analytics 52

Analytics Analytics Data Analyst Data Science 52

Pickl AI

FEBRUARY 4, 2024

Furthermore, Alteryx provides an array of tools and connectors tailored for different data sources, spanning Excel spreadsheets, databases, and social media platforms. Users can effortlessly extract data from sources like SQL Server, Excel, Tableau, and even social media platforms. Is Alteryx an ETL tool?

Data Preparation 52

Data Preparation Tableau Data Visualization SQL 52

phData

MAY 3, 2024

Its use cases range from real-time analytics, fraud detection, messaging, and ETL pipelines. It can deliver a high volume of data with latency as low as two milliseconds. It is heavily used in various industries like finance, retail, healthcare, and social media. Example: openssl rsa -in C:tmpnew_rsa_key_v1.p8

Apache Kafka 52

Apache Kafka Analytics Analytics ETL 52

phData

FEBRUARY 14, 2023

Data ingestion involves connecting your data sources, including databases, flat files, streaming data, etc, to your data warehouse. Fivetran Fivetran is a tool dedicated to replicating applications, databases, events, and files into a high-performance data warehouse, such as Snowflake.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

phData

NOVEMBER 11, 2024

These tasks often go through several stages, similar to the ETL process (Extract, Transform, Load). This means data has to be pulled from different sources (such as systems, databases, and spreadsheets), transformed (cleaned up and prepped for analysis), and then loaded back into its original spot or somewhere else when it’s done.

Data Analysis 52

Data Analysis Data Analysis Database Analytics 52

phData

FEBRUARY 5, 2024

Creating the databases, schemas, roles, and access grants that comprise a data system information architecture can be time-consuming and error-prone. Replicate can interact with a wide variety of databases, data warehouses, and data lakes (on-premise or based in the cloud).

SQL 52

SQL Database Data Quality Data Warehouse 52

Pickl AI

AUGUST 9, 2024

ETL Tools Extract, Transform, Load (ETL) tools like Talend, Informatica, and Apache Nifi enable the integration and transformation of data from source systems into the dimensional model, ensuring that hierarchies are populated correctly.

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 Engineer 40

Data Engineer Data Engineering Data Engineering Data Engineering 40

Pickl AI

AUGUST 9, 2024

Variety It encompasses the different types of data, including structured data (like databases), semi-structured data (like XML), and unstructured formats (such as text, images, and videos). Understanding the differences between SQL and NoSQL databases is crucial for students. js for creating interactive visualisations.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

APRIL 26, 2024

SQL stands for Structured Query Language, essential for querying and manipulating data stored in relational databases. The SELECT statement retrieves data from a database, while SELECT DISTINCT eliminates duplicate rows from the result set. Data Warehousing and ETL Processes What is a data warehouse, and why is it important?

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

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

phData

FEBRUARY 29, 2024

database permissions, ETL capability, processing, etc.), it has to be done using custom SQL in Tableau? Hopefully, you don’t run into this scenario because joining and querying multiple tables in Tableau using custom SQL is not recommended due to its impact on performance.

Tableau 52

Tableau SQL Database ETL 52

Pickl AI

MARCH 19, 2025

A data engineer creates and manages the pipelines that transfer data from different sources to databases or cloud storage. Data Storage : Keeping data safe in databases or cloud platforms. It allows them to retrieve, manipulate, and manage structured data in relational databases. What Does a Data Engineer Do?

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Dataconomy

MARCH 7, 2025

Businesses use it for ETL (extract, transform, load) processes, predictive modeling, and statistical analysis , making it a flexible solution for advanced data analysis. Tableau Tableau enhances data visualization with AI-driven analytics , helping users create interactive dashboards, detect patterns, and forecast trends.

AI 91

AI AI Machine Learning Machine Learning 91