O'Reilly on Data

JANUARY 17, 2023

Given that, what would you say is the job of a data scientist (or ML engineer, or any other such title)? Building Models. A common task for a data scientist is to build a predictive model. You know the drill: pull some data, carve it up into features, feed it into one of scikit-learn’s various algorithms.

Machine Learning 363

Machine Learning Predictive Modeling Software Modeling 363

O'Reilly on Data

OCTOBER 19, 2021

Let’s start by considering the job of a non-ML software engineer: writing traditional software deals with well-defined, narrowly-scoped inputs, which the engineer can exhaustively and cleanly model in the code. Not only is data larger, but models—deep learning models in particular—are much larger than before.

IT 364

IT Testing Experimentation Software 364

CIO Business Intelligence

DECEMBER 9, 2024

Data readiness and governance are critical to success and must be addressed in tandem with business process transformation. NTT DATA recommends a staggered data transformation framework so that organisations can realise value incrementally. Operations.

Technology 98

Technology Insurance Interactive Reporting 98

AWS Big Data

NOVEMBER 27, 2024

Together with price-performance, Amazon Redshift offers capabilities such as serverless architecture, machine learning integration within your data warehouse and secure data sharing across the organization. dbt Cloud is a hosted service that helps data teams productionize dbt deployments. Create dbt models in dbt Cloud.

Data Warehouse 107

Data Warehouse Analytics Testing Modeling 107

Wayne Yaddow

MARCH 14, 2025

How dbt Core aids data teams test, validate, and monitor complex data transformations and conversions Photo by NASA on Unsplash Introduction dbt Core, an open-source framework for developing, testing, and documenting SQL-based data transformations, has become a must-have tool for modern data teams as the complexity of data pipelines grows.

Data Transformation 52

Data Transformation Testing Unstructured Data Data Quality 52

CIO Business Intelligence

JANUARY 8, 2025

Secure storage, together with data transformation, monitoring, auditing, and a compliance layer, increase the complexity of the system. For example, Mosaic recently created a data-heavy Mosaic GPT safety model for mining operations on Microsofts Bing platform, and is about to roll that out in a pilot.

Data Processing 139

Data Processing Optimization Modeling Enterprise 139

AWS Big Data

JANUARY 15, 2025

In addition to real-time analytics and visualization, the data needs to be shared for long-term data analytics and machine learning applications. To achieve this, EUROGATE designed an architecture that uses Amazon DataZone to publish specific digital twin data sets, enabling access to them with SageMaker in a separate AWS account.

IoT 110

IoT Machine Learning Metadata Data-driven 110

CIO Business Intelligence

FEBRUARY 27, 2025

Data professionals need to access and work with this information for businesses to run efficiently, and to make strategic forecasting decisions through AI-powered data models. Without integrating mainframe data, it is likely that AI models and analytics initiatives will have blind spots.

Metadata 98

Metadata Data Lake Cost-Benefit Forecasting 98

AWS Big Data

OCTOBER 14, 2024

Writing SQL queries requires not just remembering the SQL syntax rules, but also knowledge of the tables metadata, which is data about table schemas, relationships among the tables, and possible column values. Generative AI models can translate natural language questions into valid SQL queries, a capability known as text-to-SQL generation.

Metadata 102

Metadata Data Lake Modeling Data Warehouse 102

Wayne Yaddow

FEBRUARY 26, 2025

AI is transforming how senior data engineers and data scientists validate data transformations and conversions. Artificial intelligence-based verification approaches aid in the detection of anomalies, the enforcement of data integrity, and the optimization of pipelines for improved efficiency.

Data Transformation 52

Data Transformation Testing Data-driven Data Quality 52

Sisense

MAY 11, 2021

You can’t talk about data analytics without talking about data modeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. Building the right data model is an important part of your data strategy.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

Dataiku

SEPTEMBER 26, 2022

Whether it’s being used as part of an Extract Transform Load process in data warehousing (or in some cases with modern cloud data stacks , ELT), for use in ML models, or simply ad hoc analysis, data transformation is a key part of nearly any project involving data.

Data Transformation 52

Data Transformation Modeling IT 52

Smart Data Collective

OCTOBER 20, 2021

In the current disruptive times, businesses depend heavily on information in real-time and data analysis techniques to make better business decisions, raising the bar for data integration. The upsurge of data (with the introduction of non-traditional data sources like streaming data, machine logs, etc.)

Data Integration 133

Data Integration Machine Learning Big Data Statistics 133

IBM Big Data Hub

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. Foundation models: The power of curated datasets Foundation models , also known as “transformers,” are modern, large-scale AI models trained on large amounts of raw, unlabeled data.

Risk 70

Risk Modeling Management Metadata 70

DataKitchen

JULY 27, 2023

Azure Databricks, a big data analytics platform built on Apache Spark, performs the actual data transformations. The cleaned and transformed data can then be stored in Azure Blob Storage or moved to Azure Synapse Analytics for further analysis and reporting. Some tools are excellent for batch processing (e.g.,

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

Dataiku

SEPTEMBER 23, 2021

We speak a lot about the ways we can use data, transform it, and create powerful models based on advanced machine learning techniques, but we sometimes forget where the data comes from initially.

Machine Learning 103

Machine Learning Data Transformation Modeling IT 103

datapine

SEPTEMBER 29, 2022

Business/Data Analyst: The business analyst is all about the “meat and potatoes” of the business. These needs are then quantified into data models for acquisition and delivery. This person (or group of individuals) ensures that the theory behind data quality is communicated to the development team. 2 – Data profiling.

Data Quality 241

Data Quality Metrics Data-driven Management 241

AWS Big Data

NOVEMBER 22, 2024

Amazon Redshift has launched a session reuse capability for the Data API that can significantly streamline multi-step, stateful workloads such as exchange, transform, and load (ETL) pipelines, reporting processes, and other flows that involve sequential queries.

Data Warehouse 104

Data Warehouse Recreation/Entertainment Cost-Benefit Data-driven 104

Jen Stirrup

SEPTEMBER 30, 2021

They may also learn from evidence, but the data and the modelling fundamentally comes from humans in some way. Data Science – Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.

Business Intelligence 135

Business Intelligence Data mining Machine Learning Testing 135

Cloudera

AUGUST 17, 2021

When we announced the GA of Cloudera Data Engineering back in September of last year, a key vision we had was to simplify the automation of data transformation pipelines at scale. Typically users need to ingest data, transform it into optimal format with quality checks, and optimize querying of the data by visual analytics tool.

Management 115

Management Cost-Benefit Data Transformation Optimization 115

CIO Business Intelligence

JULY 5, 2022

Business analytics is the practical application of statistical analysis and technologies on business data to identify and anticipate trends and predict business outcomes. Data analytics is used across disciplines to find trends and solve problems using data mining , data cleansing, data transformation, data modeling, and more.

Business Analytics 136

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 136

Ontotext

MARCH 27, 2024

For example, GPS, social media, cell phone handoffs are modeled as graphs while data catalogs, data lineage and MDM tools leverage knowledge graphs for linking metadata with semantics. Knowledge graphs model knowledge of a domain as a graph with a network of entities and relationships.

Modeling 53

Modeling Metadata Data Quality Enterprise 53

Ontotext

NOVEMBER 29, 2024

But even though technologies like Building Information Modelling (BIM) have finally introduced symbolic representation, in many ways, AECO still clings to outdated, analog practices and documents. Since the first digitization attempts were made, the modeling of built environments has also evolved.

Modeling 59

Modeling Structured Data Technology Data Transformation 59

CIO Business Intelligence

JUNE 7, 2022

Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance. What are the four types of data analytics? Data analytics includes the tools and techniques used to perform data analysis.

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

CIO Business Intelligence

DECEMBER 6, 2023

However, this partnership model cannot keep pace with an always-changing technology landscape in which the skill gaps and lack of resources are increasing. The new models recognise this, drawing tech vendors to shift toward innovation-focused roles and become partners in the client’s success.

Digital Transformation 116

Digital Transformation Software Cost-Benefit Manufacturing 116

CIO Business Intelligence

FEBRUARY 16, 2023

“All they would have to do is just build their model and run with it,” he says. But to augment its various businesses with ML and AI, Iyengar’s team first had to break down data silos within the organization and transform the company’s data operations. For now, it operates under a centralized “hub and spokes” model.

Unstructured Data 105

Unstructured Data Data Lake Prescriptive Analytics Data Warehouse 105

AWS Big Data

JULY 18, 2024

As part of the migration, reconsider your data model. In examining your data model, you can find efficiencies that dramatically improve your search latencies and throughput. Poor data modeling doesn’t only result in search performance problems but extends to other areas.

Dashboards 112

Dashboards Testing Data-driven Visualization 112

AWS Big Data

APRIL 26, 2023

We also split the data transformation into several modules (Data Aggregation, Data Filtering, and Data Preparation) to make the system more transparent and easier to maintain. Although each module is specific to a data source or a particular data transformation, we utilize reusable blocks inside of every job.

Forecasting 142

Forecasting Manufacturing Data Lake Big Data 142

AWS Big Data

NOVEMBER 15, 2023

The difference lies in when and where data transformation takes place. In ETL, data is transformed before it’s loaded into the data warehouse. In ELT, raw data is loaded into the data warehouse first, then it’s transformed directly within the warehouse.

Dashboards 111

Dashboards Analytics Metadata Data Warehouse 111

CIO Business Intelligence

FEBRUARY 20, 2024

A number of industry leaders are already experimenting with advanced AI use cases, including Denso, a leading mobility supplier that develops advanced technology and components for nearly every vehicle make and model on the road today. Denso uses AI to verify the structuring of unstructured data from across its organisation.

Data Architecture 98

Data Architecture Unstructured Data Manufacturing IT 98

CIO Business Intelligence

JUNE 29, 2022

Taking the broadest possible interpretation of data analytics , Azure offers more than a dozen services — and that’s before you include Power BI, with its AI-powered analysis and new datamart option , or governance-oriented approaches such as Microsoft Purview. Azure Data Factory. Azure Synapse Analytics. Analytics, Microsoft Azure

Data Lake 116

Data Lake Analytics Data Warehouse Machine Learning 116

CIO Business Intelligence

JANUARY 19, 2024

The exam covers everything from fundamental to advanced data science concepts such as big data best practices, business strategies for data, building cross-organizational support, machine learning, natural language processing, scholastic modeling, and more.

Data Science 126

Data Science Machine Learning Predictive Modeling Forecasting 126

CIO Business Intelligence

DECEMBER 22, 2022

It seeks to improve the way data are managed and products are created, and to coordinate these improvements with the goals of the business. According to Gartner, DataOps also aims “to deliver value faster by creating predictable delivery and change management of data, data models, and related artifacts.”

Analytics 130

Analytics Machine Learning Data mining Software 130

AWS Big Data

APRIL 11, 2023

Finally, we can use Amazon SageMaker to build forecasting models that can predict inventory demand and optimize stock levels. ElastiCache manages the real-time application data caching, allowing your customers to experience microsecond response times while supporting high-throughput handling of hundreds of millions of operations per second.

Forecasting 96

Forecasting Management IoT Data-driven 96

David Menninger's Analyst Perspectives

NOVEMBER 7, 2024

The difference is in using advanced modeling and data management to make faster scenario planning possible, driven by actionable key performance measures that enable faster, well-informed decision cycles. Predictive analytics applies machine learning to statistical modeling and historical data to make predictions about future outcomes.

Forecasting 130

Forecasting Machine Learning Finance Predictive Analytics 130

Analytics Vidhya

MAY 13, 2024

Introduction Transformers have revolutionized various domains of machine learning, notably in natural language processing (NLP) and computer vision. Their ability to capture long-range dependencies and handle sequential data effectively has made them a staple in every AI researcher and practitioner’s toolbox.

Machine Learning 327

Machine Learning Analytics IT Forecasting 327

erwin

MARCH 4, 2021

So companies will be forced to classify their data and to find mechanisms to share it with such platforms.”. GDPR is also proving to be the de facto model for data privacy across the United States. You can request a demo of erwin Data Intelligence here.

Data Governance 122

Data Governance Digital Transformation Data-driven Cost-Benefit 122