Smart Data Collective

APRIL 29, 2020

The extraction of raw data, transforming to a suitable format for business needs, and loading into a data warehouse. Data transformation. This process helps to transform raw data into clean data that can be analysed and aggregated. Data analytics and visualisation.

Data Warehouse 99

Data Warehouse SQL Azure ETL 99

Alation

JANUARY 17, 2023

It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Data orchestration tools. Business intelligence (BI) platforms. Better Data Culture.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

Pickl AI

JULY 3, 2023

What is Business Intelligence? Business Intelligence (BI) refers to the technology, techniques, and practises that are used to gather, evaluate, and present information about an organisation in order to assist decision-making and generate effective administrative action. billion in 2015 and reached around $26.50

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

Data analytics is a task that resides under the data science umbrella and is done to query, interpret and visualize datasets. Data scientists will often perform data analysis tasks to understand a dataset or evaluate outcomes. And you should have experience working with big data platforms such as Hadoop or Apache Spark.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

IBM Journey to AI blog

JULY 17, 2023

Within watsonx.ai, users can take advantage of open-source frameworks like PyTorch, TensorFlow and scikit-learn alongside IBM’s entire machine learning and data science toolkit and its ecosystem tools for code-based and visual data science capabilities. ” Vitaly Tsivin, EVP Business Intelligence at AMC Networks.

AI 74

AI AI Machine Learning Machine Learning 74

AWS Machine Learning Blog

JUNE 25, 2024

To create and share customer feedback analysis without the need to manage underlying infrastructure, Amazon QuickSight provides a straightforward way to build visualizations, perform one-time analysis, and quickly gain business insights from customer feedback, anytime and on any device. The LLM generates output based on the user prompt.

AWS 132

AWS Natural Language Processing Machine Learning Machine Learning 132

ODSC - Open Data Science

JANUARY 30, 2024

This involves extracting data from various sources, transforming it into a usable format, and loading it into data warehouses or other storage systems. Think of it as building plumbing for data to flow smoothly throughout the organization.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

The MLOps Blog

MAY 17, 2023

Focus Area ETL helps to transform the raw data into a structured format that can be easily available for data scientists to create models and interpret for any data-driven decision. A data pipeline is created with the focus of transferring data from a variety of sources into a data warehouse.

ETL 59

ETL Data Pipeline ML ML 59

Alation

JULY 19, 2022

Finally, a data catalog can help data scientists find answers to their questions (and avoid re-asking questions that have already been answered). Modern data catalogs surface a wide range of data asset types. Explore the Data. Data scientists often have different requirements for a data catalog than data analysts.

Data Scientist 59

Data Scientist Data Quality Data Science Data Analyst 59

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

ODSC - Open Data Science

JANUARY 18, 2024

Data scientists typically have strong skills in areas such as Python, R, statistics, machine learning, and data analysis. Believe it or not, these skills are valuable in data engineering for data wrangling, model deployment, and understanding data pipelines.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

phData

OCTOBER 11, 2023

Consider a scenario: a data repository residing within a cloud-based data warehouse. KNIME has become a handy tool to prepare and optimize the data. Microsoft Power BI is a dynamic and interactive data visualization platform primarily focusing on business intelligence.

Power BI 45

Power BI Data Preparation Analytics Analytics 45

Alation

JANUARY 26, 2023

Lack of data governance can summon a whole range of problems, including: Lack of consistency For data to be useful, it should be consistent across all areas. A field might not be entered in the same way across different departments, which makes the data difficult to find and affects the accuracy of business intelligence (BI).

Data Governance 52

Data Governance Data Engineer Data Engineering Data Engineering 52

DagsHub

AUGUST 23, 2024

TDWI Data Quality Framework This framework , developed by the Data Warehousing Institute, focuses on practical methodologies and tools that address managing data quality across various stages of the data lifecycle, including data integration, cleaning, and validation. quality) for your data.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Pickl AI

JULY 29, 2024

Some key applications of Hadoop clusters in big data include: Data Warehousing Hadoop clusters can be used as cost-effective data warehousing solutions , storing and processing large volumes of data for business intelligence and reporting purposes.

Hadoop 52

Hadoop Clustering Big Data Big Data 52

Data Science Blog

SEPTEMBER 3, 2024

Dabei arbeiten wir technologie-offen und mit nahezu allen Tools – Und oft in enger Verbindung mit Initiativen der Business Intelligence und Data Science. Celonis versucht Machine Learning innerhalb der Plattform aus einer Hand anzubieten und hat auch eigene Python-Bibleotheken dafür entwickelt.

Data Science 130

Data Science Power BI Azure Data Warehouse 130

Flipboard

MAY 15, 2025

Data environments in data-driven organizations are changing to meet the growing demands for analytics , including business intelligence (BI) dashboarding, one-time querying, data science , machine learning (ML), and generative AI. Amazon SageMaker Unified Studio , Amazon EMR 7.5.0 and higher, and AWS Glue 5.0

AWS 138

AWS SQL Data Lakes Data Warehouse 138

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

Flipboard

MAY 9, 2025

This pattern creates a comprehensive solution that transforms raw social media data into actionable business intelligence (BI) through advanced AI capabilities. 3B Instruct Amazon Bedrock, the system provides tailored marketing content that adds business value. By integrating LLMs such as Anthropics Claude 3.5

AWS 100

AWS Natural Language Processing AI AI 100

Pickl AI

FEBRUARY 23, 2025

Key Features : Speed : Spark processes data in-memory, making it up to 100 times faster than Hadoop MapReduce in certain applications. Ease of Use : Supports multiple programming languages including Python, Java, and Scala. Key Features : Serverless Architecture : No need for infrastructure management.

Big Data 52

Big Data Big Data Apache Hadoop Apache Kafka 52