Smart Data Collective

SEPTEMBER 19, 2022

Historical financial big data helps businesses scrutinize evolving customer behaviors, allowing them to come up with invaluable products and services that streamline banking processes. However, to take full advantage of big data’s powerful capabilities, choosing BI and ETL solutions cannot be over-emphasized.

Big Data 145

Big Data Big Data Big Data Analytics Big Data Analytics 145

ODSC - Open Data Science

FEBRUARY 6, 2024

Apache Kafka For data engineers dealing with real-time data, Apache Kafka is a game-changer. 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.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

ODSC - Open Data Science

JANUARY 30, 2024

Not only does it involve the process of collecting, storing, and processing data so that it can be used for analysis and decision-making, but these professionals are responsible for building and maintaining the infrastructure that makes this possible; and so much more. Think of data engineers as the architects of the data ecosystem.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

phData

SEPTEMBER 5, 2023

Google Analytics 4 (GA4) is a powerful tool for collecting and analyzing website and app data that many businesses rely heavily on to make informed business decisions. By the end of this tutorial, you’ll have a seamless pipeline that fetches and syncs your GA4 raw events data to Snowflake efficiently.

Azure 52

Azure Analytics Analytics Data Pipeline 52

Tableau

MAY 19, 2021

Fortunately, a modern data stack (MDS) using Fivetran, Snowflake, and Tableau makes it easier to pull data from new and various systems, combine it into a single source of truth, and derive fast, actionable insights. What is a modern data stack? Transparency .

Tableau 52

Tableau Cloud Data Data Pipeline Analytics 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. Diagnostic analytics: Diagnostic analytics helps pinpoint the reason an event occurred.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

Alation

MAY 31, 2023

If you’re not familiar with DGIQ, it’s the world’s most comprehensive event dedicated to, you guessed it, data governance and information quality. This year’s DGIQ West will host tutorials, workshops, seminars, general conference sessions, and case studies for global data leaders. His major takeaway?

Data Governance 52

Data Governance DataOps Data Pipeline Business Intelligence 52

ODSC - Open Data Science

JANUARY 18, 2024

Data engineers will also work with data scientists to design and implement data pipelines; ensuring steady flows and minimal issues for data teams. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable. Interested in attending an ODSC event?

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

AWS Machine Learning Blog

APRIL 12, 2023

Traditional maintenance activities rely on a sizable workforce distributed across key locations along the BHS dispatched by operators in the event of an operational fault. From now on, we will launch a retraining every 3 months and, as soon as possible, will use up to 1 year of data to account for the environmental condition seasonality.

AWS 116

AWS ML ML Machine Learning 116

IBM Journey to AI blog

JULY 13, 2023

This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches. Effective data security protocols and tools contribute to strong data integrity.

Data Quality 74

Data Quality Data Profiling Data Governance Analytics 74

IBM Journey to AI blog

OCTOBER 16, 2023

Curated foundation models, such as those created by IBM or Microsoft, help enterprises scale and accelerate the use and impact of the most advanced AI capabilities using trusted data. In addition to natural language, models are trained on various modalities, such as code, time-series, tabular, geospatial and IT events data.

AI 88

AI AI Data Warehouse ML 88

ODSC - Open Data Science

MAY 10, 2023

Unfortunately, even the data science industry — which should recognize tabular data’s true value — often underestimates its relevance in AI. Many mistakenly equate tabular data with business intelligence rather than AI, leading to a dismissive attitude toward its sophistication.

Data Scientist 87

Data Scientist Data Science Deep Learning Deep Learning 87

Pickl AI

JULY 25, 2024

Data Warehouse: Its significance and relevance in the data world. Exploring Differences: Database vs Data Warehouse. Role in Data Warehousing and Business Intelligence Dimensional modelling plays a crucial role in data warehousing and business intelligence by structuring data to enhance performance and usability.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

Mlearning.ai

FEBRUARY 16, 2023

Today, companies are facing a continual need to store tremendous volumes of data. The demand for information repositories enabling business intelligence and analytics is growing exponentially, giving birth to cloud solutions. Data warehousing is a vital constituent of any business intelligence operation.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

ODSC - Open Data Science

SEPTEMBER 29, 2023

In today’s digital world, data is king. Organizations that can capture, store, format, and analyze data and apply the business intelligence gained through that analysis to their products or services can enjoy significant competitive advantages. But, the amount of data companies must manage is growing at a staggering rate.

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

phData

DECEMBER 12, 2023

A complete view of the fan, rather than pieces of information spread across various departments, means less guesswork and more data insights. Step 2: Analyze the Data Once you have centralized your data, use a business intelligence tool like Sigma Computing , Power BI , Tableau , or another to craft analytics dashboards.

Data Warehouse 52

Data Warehouse Machine Learning Machine Learning Tableau 52

Alation

APRIL 14, 2022

Data pipeline orchestration. Moving/integrating data in the cloud/data exploration and quality assessment. Supports the ability to interact with the actual data and perform analysis on it. This provides the facility a time or event for a job to run and offers useful post-run information. Scheduling.

Data Governance 59

Data Governance ML ML Cloud Data 59

DagsHub

AUGUST 23, 2024

Data Quality Dimensions Data quality dimensions are the criteria that are used to evaluate and measure the quality of data. These include the following: Accuracy indicates how correctly data reflects the real-world entities or events it represents.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

The MLOps Blog

MARCH 21, 2023

Other users Some other users you may encounter include: Data engineers , if the data platform is not particularly separate from the ML platform. Analytics engineers and data analysts , if you need to integrate third-party business intelligence tools and the data platform, is not separate.

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69