Flipboard

DECEMBER 11, 2024

SageMaker Unied Studio is an integrated development environment (IDE) for data, analytics, and AI. Discover your data and put it to work using familiar AWS tools to complete end-to-end development workflows, including data analysis, data processing, model training, generative AI app building, and more, in a single governed environment.

SQL 160

SQL AWS Data Lakes ML 160

phData

JULY 10, 2024

Sigma Computing , a cloud-based analytics platform, helps data analysts and business professionals maximize their data with collaborative and scalable analytics. One of Sigma’s key features is its support for custom SQL queries and CSV file uploads. These tools allow users to handle more advanced data tasks and analyses.

SQL 52

SQL Data Warehouse Analytics Analytics 52

Smart Data Collective

APRIL 29, 2020

The storage and processing of data through a cloud-based system of applications. Master data management. The techniques for managing organisational data in a standardised approach that minimises inefficiency. Extraction, Transform, Load (ETL). Data transformation.

Data Warehouse 99

Data Warehouse SQL Azure ETL 99

Pickl AI

NOVEMBER 5, 2024

It allows developers to easily connect to databases, execute SQL queries, and retrieve data. It operates as an intermediary, translating Java calls into SQL commands the database understands. For instance, reporting and analytics tools commonly use it to pull data from various database systems. from 2023 to 2030.

Database 52

Database SQL Python Database Administration 52

Data Science Dojo

JULY 3, 2024

Here’s a list of key skills that are typically covered in a good data science bootcamp: Programming Languages : Python : Widely used for its simplicity and extensive libraries for data analysis and machine learning. R : Often used for statistical analysis and data visualization.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Pickl AI

MARCH 3, 2025

Summary: Business Intelligence Analysts transform raw data into actionable insights. They use tools and techniques to analyse data, create reports, and support strategic decisions. Key skills include SQL, data visualization, and business acumen. Introduction We are living in an era defined by data.

Business Intelligence 52

Business Intelligence Business Intelligence Data Analyst Data Visualization 52

Women in Big Data

NOVEMBER 27, 2024

Evaluate integration capabilities with existing data sources and Extract Transform and Load (ETL) tools. Its PostgreSQL foundation ensures compatibility with most SQL clients. Strengths : Real-time analytics, built-in machine learning capabilities, and fast querying with standard SQL.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

Pickl AI

JULY 6, 2023

Hive is a data warehousing infrastructure built on top of Hadoop. It has the following features: It facilitates querying, summarizing, and analyzing large datasets Hadoop also provides a SQL-like language called HiveQL Hive allows users to write queries to extract valuable insights from structured and semi-structured data stored in Hadoop.

Hadoop 52

Hadoop SQL Big Data Big Data 52

Pickl AI

APRIL 26, 2024

This comprehensive blog outlines vital aspects of Data Analyst interviews, offering insights into technical, behavioural, and industry-specific questions. It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

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 Engineer Data Engineering 52

ODSC - Open Data Science

APRIL 3, 2023

We looked at over 25,000 job descriptions, and these are the data analytics platforms, tools, and skills that employers are looking for in 2023. Excel is the second most sought-after tool in our chart as you’ll see below as it’s still an industry standard for data management and analytics.

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

ODSC - Open Data Science

JUNE 12, 2023

They all agree that a Datamart is a subject-oriented subset of a data warehouse focusing on a particular business unit, department, subject area, or business functionality. The Datamart’s data is usually stored in databases containing a moving frame required for data analysis, not the full history of data.

Power BI 52

Power BI Data Warehouse ETL Data Preparation 52

AWS Machine Learning Blog

JUNE 25, 2024

In addition, the generative business intelligence (BI) capabilities of QuickSight allow you to ask questions about customer feedback using natural language, without the need to write SQL queries or learn a BI tool.

AWS 125

AWS Natural Language Processing Machine Learning Machine Learning 125

Dataconomy

MARCH 24, 2023

BI involves using data mining, reporting, and querying techniques to identify key business metrics and KPIs that can help companies make informed decisions. A career path in BI can be a lucrative and rewarding choice for those with interest in data analysis and problem-solving. How to become a blockchain maestro?

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Dataconomy

MARCH 24, 2023

BI involves using data mining, reporting, and querying techniques to identify key business metrics and KPIs that can help companies make informed decisions. A career path in BI can be a lucrative and rewarding choice for those with interest in data analysis and problem-solving. How to become a blockchain maestro?

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Pickl AI

NOVEMBER 15, 2023

What Is a Data Warehouse? On the other hand, a Data Warehouse is a structured storage system designed for efficient querying and analysis. It involves the extraction, transformation, and loading (ETL) process to organize data for business intelligence purposes. It often serves as a source for Data Warehouses.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

Pickl AI

NOVEMBER 14, 2023

Your journey ends here where you will learn the essential handy tips quickly and efficiently with proper explanations which will make any type of data importing journey into the Python platform super easy. Introduction Are you a Python enthusiast looking to import data into your code with ease?

Python 52

Python SQL Database Data Analysis 52

Dataconomy

JULY 28, 2023

Proper data preprocessing is essential as it greatly impacts the model performance and the overall success of data analysis tasks ( Image Credit ) Data integration Data integration involves combining data from various sources and formats into a unified and consistent dataset.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Pickl AI

SEPTEMBER 19, 2024

Use Cases of Hadoop Hadoop is widely used in finance, healthcare, and retail industries for fraud detection, risk analysis, customer segmentation, and large-scale data storage. It also supports ETL (Extract, Transform, Load) processes, making data warehousing and analytics essential. What is Apache Spark?

Hadoop 52

Hadoop Big Data Big Data Clustering 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. Data Analytics automation Alteryx’s standout feature lies in its capability to automate data analytics workflows. Is Alteryx an ETL tool?

Data Preparation 52

Data Preparation Tableau Data Visualization Analytics 52

ODSC - Open Data Science

JANUARY 18, 2024

Like with any professional shift, it’s always good practice to take inventory of your existing data science strengths. Data scientists typically have strong skills in areas such as Python, R, statistics, machine learning, and data analysis. With that said, each skill may be used in a different manner.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

AUGUST 9, 2024

Improved Data Navigation Hierarchies provide a clear structure for users to navigate through data. Enhanced Data Analysis By allowing users to drill down into data, hierarchies enable more detailed analysis. They enable intuitive querying and reporting by providing a clear structure for data exploration.

Data Warehouse 52

Data Warehouse Data Quality ETL Business Intelligence 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. These tools work together to facilitate efficient data management and analysis processes.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

Pickl AI

NOVEMBER 4, 2024

It enables reporting and Data Analysis and provides a historical data record that can be used for decision-making. Key components of data warehousing include: ETL Processes: ETL stands for Extract, Transform, Load. ETL is vital for ensuring data quality and integrity.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

The MLOps Blog

OCTOBER 20, 2023

And that’s what we’re going to focus on in this article, which is the second in my series on Software Patterns for Data Science & ML Engineering. I’ll show you best practices for using Jupyter Notebooks for exploratory data analysis. When data science was sexy , notebooks weren’t a thing yet. documentation.

SQL 52

SQL Database Data Scientist Python 52

phData

SEPTEMBER 4, 2024

ThoughtSpot is a cloud-based AI-powered analytics platform that uses natural language processing (NLP) or natural language query (NLQ) to quickly query results and generate visualizations without the user needing to know any SQL or table relations. Suppose your business requires more robust capabilities across your technology stack.

Analytics 52

Analytics Analytics SQL ETL 52

phData

JANUARY 4, 2023

With Snowflake, organizations can be data consumers, data providers, or both. Complete SQL Database No need to learn new tools as Snowflake supports the tools millions of business users already know how to use today.

Data Warehouse 52

Data Warehouse SQL Database ETL 52

Tableau

AUGUST 21, 2023

Finally, Tableau allows you to create custom territories using Tableau groups and overlay data with demographic information, giving you a comprehensive view of your data. ODBC is an industry standard that allows various software to access data.

Tableau 97

Tableau Database Analytics Analytics 97

Pickl AI

DECEMBER 16, 2024

Its core components include: Lakehouse : Offers robust data storage and processing capabilities. Data Factory : Simplifies the creation of ETL pipelines to integrate data from diverse sources. Developed by Microsoft, it is designed to simplify Data Analysis for users at all levels, from beginners to advanced analysts.

Power BI 52

Power BI Analytics Analytics Machine Learning 52

phData

SEPTEMBER 19, 2023

This technique accommodates diverse and evolving data formats and schemas, enabling agility and exploration within the data lake. There are tools designed specifically to analyze your data lake files, determine the schema, and allow for SQL statements to be run directly off this data.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

DagsHub

OCTOBER 23, 2024

Here’s the structured equivalent of this same data in tabular form: With structured data, you can use query languages like SQL to extract and interpret information. In contrast, such traditional query languages struggle to interpret unstructured data. is similar to the traditional Extract, Transform, Load (ETL) process.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Flipboard

MARCH 21, 2025

Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems. Users can write data to managed RMS tables using Iceberg APIs, Amazon Redshift, or Zero-ETL ingestion from supported data sources.

SQL 136

SQL Data Analyst Data Warehouse AWS 136

phData

APRIL 20, 2023

Snowflake’s managed data processing unit called “Tasks” wakes up at a defined interval and checks for data in the associated stream. If data is present, Tasks runs SQL to push it to the raw data vault objects. This setup facilitates tracking of sensitive data usage and reduced access control management overheads.

Data Warehouse 72

Data Warehouse Data Governance Clustering Database 72

Pickl AI

MARCH 10, 2025

In this blog, well explore the 5 key components of Power BI , their features, and how they can help you make data-driven decisions. Key Takeaways User-Friendly Interface: Simplifies data analysis for non-technical users. Key Features Data Import: Connects to multiple data sources like Excel, SQL Server, or cloud services.

Power BI 52

Power BI Business Intelligence Business Intelligence Analytics 52

ODSC - Open Data Science

DECEMBER 9, 2024

This allows iterative data analysis workflows rather than rigid scripts. Python forms a common lingua franca for open data science thanks to its flexibility and the breadth of domain-specific packages continuously expanded by the active community. automatically produces visualizationsno SQL query or Python coding required.

Data Science 40

Data Science Machine Learning Machine Learning Python 40

Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52