DataKitchen

OCTOBER 25, 2022

The goal of DataOps Observability is to provide visibility of every journey that data takes from source to customer value across every tool, environment, data store, data and analytic team, and customer so that problems are detected, localized and raised immediately. A data journey spans and tracks multiple pipelines.

Testing 130

Testing Data-driven Visualization Dashboards 130

AWS Big Data

NOVEMBER 13, 2023

The downstream consumers consist of business intelligence (BI) tools, with multiple data science and data analytics teams having their own WLM queues with appropriate priority values. Consequently, there was a fivefold rise in data integrations and a fivefold increase in ad hoc queries submitted to the Redshift cluster.

Data Warehouse 110

Data Warehouse Analytics Data Lake Data Science 110

Cloudera

JUNE 24, 2024

In the fast-evolving landscape of data science and machine learning, efficiency is not just desirable—it’s essential. Imagine a world where every data practitioner, from seasoned data scientists to budding developers, has an intelligent assistant at their fingertips.

Machine Learning 72

Machine Learning Data Science Data-driven Testing 72

AWS Big Data

OCTOBER 14, 2024

Enterprise data is brought into data lakes and data warehouses to carry out analytical, reporting, and data science use cases using AWS analytical services like Amazon Athena , Amazon Redshift , Amazon EMR , and so on. A question arises on what level of details we need to include in the table metadata.

Metadata 105

Metadata Data Lake Modeling Data Warehouse 105

Cloudera

OCTOBER 11, 2021

Modak Nabu automates repetitive tasks in the data preparation process and thus accelerates the data preparation by 4x. Modak Nabu relies on a framework of “Botworks”, a series of micro-jobs to accomplish various data transformation steps from ingestion to profiling, and indexing. Cloud Speed and Scale.

Data Lake 95

Data Lake Cost-Benefit Data-driven Dashboards 95

Sisense

AUGUST 6, 2020

Be sure test cases represent the diversity of app users. As an AI product manager, here are some important data-related questions you should ask yourself: What is the problem you’re trying to solve? What data transformations are needed from your data scientists to prepare the data? The perfect fit.

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

Cloudera

NOVEMBER 29, 2021

Data transforms businesses. That’s where the data lifecycle comes into play. Managing data and its flow, from the edge to the cloud, is one of the most important tasks in the process of gaining data intelligence. .

Data Lake 77

Data Lake Data Warehouse Data Architecture Reporting 77

Insight

APRIL 8, 2020

Having run a data engineering program at Insight for several years, we’ve identified three broad categories of data engineers: Software engineers who focus on building data pipelines. In some cases, they work to deploy data science models into production with an eye towards optimization, scalability and maintainability.

Data Warehouse 96

Data Warehouse Cost-Benefit Software Optimization 96

Domino Data Lab

AUGUST 20, 2019

The problem is that a new unique identifier of a test example won’t be anywhere in the tree. Feature extraction means moving from low-level features that are unsuitable for learning—practically speaking, we get poor testing results—to higher-level features which are useful for learning. Separate out a hold-out test set.

Testing 68

Testing Modeling Interactive Measurement 68

Alation

JANUARY 17, 2023

Powered by cloud computing, more data professionals have access to the data, too. Data analysts have access to the data warehouse using BI tools like Tableau; data scientists have access to data science tools, such as Dataiku. Better Data Culture. Good data warehouses should be reliable.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Science Data Transformation 52

Cloudera

AUGUST 8, 2022

No more lock-in, unnecessary data transformations, or data movement across tools and clouds just to extract insights out of the data. Exploratory data science and visualization: Access Iceberg tables through auto-discovered CDW connection in CML projects.

Snapshot 94

Snapshot Data Warehouse Machine Learning Cost-Benefit 94

AWS Big Data

AUGUST 6, 2024

Example data The following code shows an example of raw order data from the stream: Record1: { "orderID":"101", "email":" john. To address the challenges with the raw data, we can implement a comprehensive data transformation process using Redshift ML integrated with an LLM in an ETL workflow.

Data Warehouse 104

Data Warehouse Data-driven Modeling Internet of Things 104

Alation

FEBRUARY 20, 2020

In perhaps a preview of things to come next year, we decided to test how a Data Catalog might work with Tableau on the same data. You can check out a self service data prep flow from catalog to viz in this recorded version here. Rita Sallam Introduces the Data Prep Rodeo. It’s a process.

Scorecard 52

Scorecard ROI Data-driven Visualization 52

IBM Big Data Hub

JULY 17, 2023

By supporting open-source frameworks and tools for code-based, automated and visual data science capabilities — all in a secure, trusted studio environment — we’re already seeing excitement from companies ready to use both foundation models and machine learning to accomplish key tasks. Within the watsonx.ai

Machine Learning 62

Machine Learning Data Warehouse Modeling Cost-Benefit 62

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. But what does this mean from a practitioner perspective?

Metrics 52

Metrics Dashboards Sales Reporting 52

DataRobot Blog

MARCH 10, 2022

With Snowflake’s newest feature release, Snowpark , developers can now quickly build and scale data-driven pipelines and applications in their programming language of choice, taking full advantage of Snowflake’s highly performant and scalable processing engine that accelerates the traditional data engineering and machine learning life cycles.

Manufacturing 52

Manufacturing IoT Machine Learning Forecasting 52

FineReport

MARCH 31, 2024

Through meticulous testing and research, we’ve curated a list of the ten best BI tools, ensuring accessibility and efficacy for businesses of all sizes. In essence, the core capabilities of the best BI tools revolve around four essential functions: data integration, data transformation, data visualization, and reporting.

Dashboards 52

Dashboards Visualization Data mining Data-driven 52

Peter James Thomas

OCTOBER 30, 2018

It may well be that one thing that a CDO needs to get going is a data transformation programme. This may purely be focused on cultural aspects of how an organisation records, shares and otherwise uses data. It may be to build a new (or a first) Data Architecture. Creating and managing a Data Science capability.

Marketing 51

Marketing Strategy Data Architecture Data Strategy 51

DataRobot Blog

APRIL 4, 2022

Before the data is put into the model comes a process called feature engineering – transforming the original data columns to impose certain business assumptions or simply increase model accuracy. The classical approach is to assume the adstock function (typically linear ) and test out various values of ? Request a demo.

Machine Learning 52

Machine Learning Sales Measurement ROI 52

CIO Business Intelligence

AUGUST 7, 2024

The Project Kernel framework utilizes templates and AI augmentation to streamline coding processes, with the AI augmentation generating test cases using training models built on the organization’s data, use cases, and past test cases. This enabled the team to expose the technology to a small group of senior leaders to test.

IT 119

IT Insurance Cost-Benefit Testing 119

AWS Big Data

DECEMBER 17, 2024

Conduct data quality tests on anonymized data in compliance with data policies Conduct data quality tests to quickly identify and address data quality issues, maintaining high-quality data at all times. The challenge Data quality tests require performing 1,300 tests on 10 TB of data monthly.

Data Quality 94

Data Quality Testing Metrics Optimization 94