datapine

SEPTEMBER 29, 2022

What Is Data Quality Management (DQM)? Data quality management is a set of practices that aim at maintaining a high quality of information. It goes all the way from the acquisition of data and the implementation of advanced data processes, to an effective distribution of data.

Data Quality 241

Data Quality Metrics Data-driven Management 241

datapine

MAY 2, 2023

Table of Contents 1) Benefits Of Big Data In Logistics 2) 10 Big Data In Logistics Use Cases Big data is revolutionizing many fields of business, and logistics analytics is no exception. The complex and ever-evolving nature of logistics makes it an essential use case for big data applications. Did you know?

Big Data 275

Big Data Cost-Benefit Internet of Things Optimization 275

AWS Big Data

MARCH 16, 2023

This post is co-written by Anish Moorjani, Data Engineer at SafetyCulture. Amazon Redshift is a fully managed data warehouse service that tens of thousands of customers use to manage analytics at scale. A source of unpredictable workloads is dbt Cloud , which SafetyCulture uses to manage data transformations in the form of models.

Data Warehouse 52

Data Warehouse Testing Snapshot Modeling 52

AWS Big Data

JULY 26, 2023

To grow the power of data at scale for the long term, it’s highly recommended to design an end-to-end development lifecycle for your data integration pipelines. The following are common asks from our customers: Is it possible to develop and test AWS Glue data integration jobs on my local laptop?

Data Integration 69

Data Integration Snapshot Testing Visualization 69

Octopai

OCTOBER 6, 2022

We’re not sure if data assets experience stress when they have to migrate to a new residence, but certainly all of the humans involved in the data migration process do! What’s a time-tested strategy to ease the packing-and-unpacking part of moving house? Duplicate data Outdated data Redundant data Irrelevant data.

Testing 52

Testing Visualization Data Transformation Strategy 52

AWS Big Data

FEBRUARY 26, 2024

When you start the process of designing your data model for Amazon Keyspaces, it’s essential to possess a comprehensive understanding of your access patterns, similar to the approach used in other NoSQL databases. It empowers businesses to explore and gain insights from large volumes of data quickly.

Dashboards 60

Dashboards Testing Metrics Optimization 60

O'Reilly on Data

OCTOBER 19, 2021

Why: Data Makes It Different. 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. However, the concept is quite abstract. How can you start applying the stack in practice today?

IT 346

IT Testing Experimentation Software 346

CDW Research Hub

OCTOBER 25, 2019

As we review data transformation and modernization strategies with our clients, we find many are investigating Snowflake as a data warehouse solution due to its ease of use, speed, and increased flexibility over a traditional data warehouse offering. Snowflake supports data that is structured, delimited (CSV, TSV, etc.)

Data Warehouse 40

Data Warehouse Testing Strategy Data-driven 40

Cloudera

JULY 21, 2022

Cloudera users can securely connect Rill to a source of event stream data, such as Cloudera DataFlow , model data into Rill’s cloud-based Druid service, and share live operational dashboards within minutes via Rill’s interactive metrics dashboard or any connected BI solution. Data is made queryable in real time.

Metrics 88

Metrics Slice and Dice Data Warehouse Dashboards 88

Cloudera

OCTOBER 13, 2020

CDP Machine Learning: a kubernetes-based service that allows data scientists to deploy collaborative workspaces with secure, self-service access to enterprise data. Predict – Data Engineering (Apache Spark). Enable Cloud Everywhere : Accelerate adoption of cloud-native data services for public clouds . New Services.

Data Warehouse 97

Data Warehouse Machine Learning Visualization Data Lake 97

Cloudera

FEBRUARY 8, 2021

Development Environment for Data Scientists, Isolated, Containerized, and Elastic. Production ML Toolkit – Deploying, Serving, Monitoring, and Governance of ML models. Simple, drag-and-drop building of dashboards and apps with Cloudera Data Visualization. Now, let’s start testing our model! and run it.

Machine Learning 76

Machine Learning Statistics Modeling Dashboards 76

Cloudera

DECEMBER 21, 2021

Packaging Apache Airflow and exposing it as a managed service within CDE alleviates the typical operational management overhead of security and uptime while providing data engineers a job management API to schedule and monitor multi-step pipelines. Test Drive CDP Pubic Cloud.

Snapshot 120

Snapshot Data-driven Optimization Management 120

AWS Big Data

MARCH 13, 2024

The solution consists of the following interfaces: IoT or mobile application – A mobile application or an Internet of Things (IoT) device allows the tracking of a company vehicle while it is in use and transmits its current location securely to the data ingestion layer in AWS. The ingestion approach is not in scope of this post.

Analytics 90

Analytics IoT Metadata Internet of Things 90

DataRobot Blog

MARCH 10, 2022

These initiatives utilize interconnected devices and automated machines that create a hyperbolic increase in data volumes. This type of growth has stressed legacy data management systems and makes it nearly impossible to implement a profitable data-centered solution.

Manufacturing 52

Manufacturing IoT Machine Learning Forecasting 52

AWS Big Data

MAY 25, 2023

Data transformation plays a pivotal role in providing the necessary data insights for businesses in any organization, small and large. To gain these insights, customers often perform ETL (extract, transform, and load) jobs from their source systems and output an enriched dataset.

Reporting 66

Reporting Metrics Optimization Data Lake 66

erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. So questions linger about whether transformed data can be trusted.

Data Governance 40

Data Governance Risk Metadata Management 40

Insight

APRIL 8, 2020

In some cases, they work to deploy data science models into production with an eye towards optimization, scalability and maintainability. Data architects and data modelers who specialize in areas such as schema design, identifying query access patterns and building and maintaining data warehouses.

Cost-Benefit 96

Cost-Benefit Software Data Warehouse Optimization 96

DataKitchen

JULY 27, 2023

While working in Azure with our customers, we have noticed several standard Azure tools people use to develop data pipelines and ETL or ELT processes. We counted ten ‘standard’ ways to transform and set up batch data pipelines in Microsoft Azure. You can use it for big data analytics and machine learning workloads.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

O'Reilly on Data

JANUARY 17, 2023

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. You might say that the outcome of this exercise is a performant predictive model. That’s sort of true.

Machine Learning 325

Machine Learning Predictive Modeling Software Modeling 325

Smarten

MAY 13, 2024

Incorporate PMML Integration Within Augmented Analytics to Easily Manage Predictive Models! PMML is Predictive Model Markup Language. It is an interchange format that provides a method by which analytical applications and software can describe and exchange predictive models. So, what is PMML Integration?

Predictive Modeling 62

Predictive Modeling Data mining Modeling Predictive Analytics 62

AWS Big Data

APRIL 25, 2023

Customers often need to share data between disparate software as a service (SaaS) platforms within their organization or across organizations. On many occasions, they need to apply business logic to the data received from the source SaaS platform before pushing it to the target SaaS platform. Let’s take an example. Choose Create stack.

Sales 68

Sales Visualization Software Marketing 68

Ontotext

APRIL 4, 2019

We apply Artificial Intelligence techniques to understand the value locked in this data so we can extract knowledge that can benefit people. Within a large enterprise, there is a huge amount of data accumulated over the years – many decisions have been made and different methods have been tested.

Recreation/Entertainment 76

Recreation/Entertainment Testing Enterprise Knowledge Discovery 76

Peter James Thomas

OCTOBER 30, 2018

Where they have, I have normally found the people holding these roles to be better informed about data matters than their peers. Prelude… I recently came across an article in Marketing Week with the clickbait-worthy headline of Why the rise of the chief data officer will be short-lived (their choice of capitalisation).

Marketing 51

Marketing Strategy Data Architecture Data Strategy 51

Cloudera

OCTOBER 7, 2022

dbt allows data teams to produce trusted data sets for reporting, ML modeling, and operational workflows using SQL, with a simple workflow that follows software engineering best practices like modularity, portability, and continuous integration/continuous development (CI/CD). The Open Data Lakehouse . Introduction.

Data Warehouse 84

Data Warehouse Data Transformation Testing Data Lake 84

AWS Big Data

NOVEMBER 15, 2023

It seamlessly consolidates data from various data sources within AWS, including AWS Cost Explorer (and forecasting with Cost Explorer ), AWS Trusted Advisor , and AWS Compute Optimizer. Overview of the BMW Cloud Data Hub At the BMW Group, Cloud Data Hub (CDH) is the central platform for managing company-wide data and data solutions.

Dashboards 91

Dashboards Analytics Metadata Data Warehouse 91

DataKitchen

OCTOBER 5, 2022

Observability is a methodology for providing visibility of every journey that data takes from source to customer value across every tool, environment, data store, team, and customer so that problems are detected and addressed immediately. to monitor your data operations. DataOps Industry Challenges. This call is from the CEO?his

Testing 176

Testing Risk Data Processing Statistics 176

DataKitchen

OCTOBER 5, 2022

Observability is a methodology for providing visibility of every journey that data takes from source to customer value across every tool, environment, data store, team, and customer so that problems are detected and addressed immediately. to monitor your data operations. DataOps Industry Challenges. This call is from the CEO?his

Testing 130

Testing Risk Data Processing Statistics 130

DataKitchen

OCTOBER 25, 2022

Observability is a methodology for providing visibility of every journey that data takes from source to customer value across every tool, environment, data store, team, and customer so that problems are detected and addressed immediately. Data journey observability is the first step in implementing DataOps.

Testing 130

Testing Data-driven Visualization Dashboards 130

Sisense

AUGUST 6, 2020

Doing it right can mean the difference between thriving in the new world of data and disappearing from it. Be sure test cases represent the diversity of app users. Better data, better AI, better products. Having the right data (and enough of it) to train your AI is vital. Can a chatbot help improve relations?

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

Alation

OCTOBER 27, 2022

Few actors in the modern data stack have inspired the enthusiasm and fervent support as dbt. This data transformation tool enables data analysts and engineers to transform, test and document data in the cloud data warehouse. Jason: How do you use these models?

Metrics 52

Metrics Dashboards Sales Reporting 52

Alation

JANUARY 17, 2023

Chances are, you’ve heard of the term “modern data stack” before. In this article, I will explain the modern data stack in detail, list some benefits, and discuss what the future holds. It is known to have benefits in handling data due to its robustness, speed, and scalability. Extract, load, Transform (ELT) tools.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Business Intelligence 52

IBM Big Data Hub

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise. IBM watsonx.ai

Machine Learning 69

Machine Learning Data Warehouse Modeling Cost-Benefit 69

DataRobot Blog

APRIL 4, 2022

MMM stands for Marketing Mix Model and it is one of the oldest and most well-established techniques to measure the sales impact of marketing activity statistically. Data Requirements. As with any type of statistical model, data is key and GIGO (“Garbage In, Garbage Out”) principle definitely applies.

Machine Learning 52

Machine Learning Sales Measurement ROI 52

IBM Big Data Hub

JANUARY 18, 2023

A read-optimized platform that can integrate data from multiple applications emerged. In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Value of the data projects are difficult to realize. It was Datawarehouse.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

AWS Big Data

NOVEMBER 16, 2023

As with all AWS services, Amazon Redshift is a customer-obsessed service that recognizes there isn’t a one-size-fits-all for customers when it comes to data models, which is why Amazon Redshift supports multiple data models such as Star Schemas, Snowflake Schemas and Data Vault. Data Vault 2.0

Enterprise 81

Enterprise Data Warehouse Data Lake Optimization 81

AWS Big Data

OCTOBER 11, 2023

The lift and shift migration approach is limited in its ability to transform businesses because it relies on outdated, legacy technologies and architectures that limit flexibility and slow down productivity. It shows a call center streaming data source that sends the latest call center feed in every 15 seconds.

Management 101

Management Metadata Analytics Dashboards 101

AWS Big Data

MARCH 3, 2023

Tricentis is the global leader in continuous testing for DevOps, cloud, and enterprise applications. Speed changes everything, and continuous testing across the entire CI/CD lifecycle is the key. Tricentis instills that confidence by providing software tools that enable Agile Continuous Testing (ACT) at scale.

Software 52

Software Data Lake Testing Cost-Benefit 52