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

CIO Business Intelligence

JUNE 13, 2022

According to Evanta’s 2022 CIO Leadership Perspectives study, CIOs’ second top priority within the IT function is around data and analytics, with CIOs seeing advancing organizational use of data as key to reaching enterprise objectives. Others also list data initiatives as a top issue for CIOs.

IT 144

IT Digital Transformation Strategy Internet of Things 144

DataKitchen

JULY 27, 2023

You can use it for big data analytics and machine learning workloads. Azure Databricks Delta Live Table s: These provide a more straightforward way to build and manage Data Pipelines for the latest, high-quality data in Delta Lake. There Will Never Be One “Best” Data Transformation Pipeline Tool.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

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 325

Machine Learning Predictive Modeling Software Modeling 325

O'Reilly on Data

OCTOBER 19, 2021

Much has been written about struggles of deploying machine learning projects to production. As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. However, the concept is quite abstract.

IT 346

IT Testing Experimentation Software 346

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. What is MMM?

Machine Learning 52

Machine Learning Sales Measurement ROI 52

AWS Big Data

APRIL 25, 2023

The following AWS services are used for data ingestion, processing, and load: Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications like Salesforce, SAP, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift , in just a few clicks.

Sales 74

Sales Visualization Software Marketing 74

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

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). Introduction.

Data Warehouse 80

Data Warehouse Data Transformation Testing Data Lake 80

AWS Big Data

NOVEMBER 15, 2023

Once released, consumers use datasets from different providers for analysis, machine learning (ML) workloads, and visualization. Each CDH dataset has three processing layers: source (raw data), prepared (transformed data in Parquet), and semantic (combined datasets).

Dashboards 98

Dashboards Analytics Metadata Data Warehouse 98

Sisense

AUGUST 6, 2020

AI and machine learning (ML) are not just catchy buzzwords; they’re vital to the future of our planet and your business. 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. Can a chatbot help improve relations?

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

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.

Machine Learning 65

Machine Learning Data Warehouse Modeling Cost-Benefit 65

IBM Big Data Hub

JANUARY 18, 2023

They defined it as : “ A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data. ”.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

Cloudera

JUNE 30, 2022

Today’s general availability announcement covers Iceberg running within key data services in the Cloudera Data Platform (CDP) — including Cloudera Data Warehousing ( CDW ), Cloudera Data Engineering ( CDE ), and Cloudera Machine Learning ( CML ). Why integrate Apache Iceberg with Cloudera Data Platform?

Data Lake 83

Data Lake Data Architecture Metadata Data Warehouse 83

AWS Big Data

NOVEMBER 16, 2023

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

Enterprise 87

Enterprise Data Warehouse Data Lake Optimization 87

Domino Data Lab

AUGUST 20, 2019

Many thanks to AWP Pearson for the permission to excerpt “Manual Feature Engineering: Manipulating Data for Fun and Profit” from the book, Machine Learning with Python for Everyone by Mark E. Feature engineering is useful for data scientists when assessing tradeoff decisions regarding the impact of their ML models.

Testing 68

Testing Modeling Interactive Measurement 68

AWS Big Data

FEBRUARY 21, 2023

We use Apache Spark as our main data processing engine and have over 1,000 Spark applications running over massive amounts of data every day. These Spark applications implement our business logic ranging from data transformation, machine learning (ML) model inference, to operational tasks.

Cost-Benefit 56

Cost-Benefit Informatics Optimization Management 56

Cloudera

OCTOBER 14, 2020

Apache Spark unifies batch processing, real-time processing, stream analytics, machine learning, and interactive query in one-platform. The Test and Development queue have fixed resource limits. Setting up the cluster to organization hierarchy based resource allocation model. Background. Why choose K8s for Apache Spark.

Machine Learning 113

Machine Learning Management Big Data Optimization 113

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 76

Data Lake Data Warehouse Data Architecture Reporting 76

AWS Big Data

FEBRUARY 6, 2023

The most common use case for Airflow is ETL (extract, transform, and load). Operationalizing machine learning (ML) is another growing use case, where data has to be transformed and normalized before it can be loaded into an ML model. format(S3_BUCKET_NAME), 's3://{}/data/aggregated/green'.format(S3_BUCKET_NAME),

Management 87

Management Interactive Metadata Publishing 87

CIO Business Intelligence

OCTOBER 11, 2022

The goal, she explained, is to knock down data silos between those groups, using multiple data lakes supported by strong security and governance, to drive positive impact across the supply chain, manufacturing, and the clinical trials of new drugs. . Four ways to improve data-driven business transformation . AstraZeneca.

Machine Learning 145

Machine Learning Data-driven Testing Data Science 145

DataKitchen

APRIL 26, 2021

GSK had been pursuing DataOps capabilities such as automation, containerization, automated testing and monitoring, and reusability, for several years. Furthermore, the introduction of AI and ML models hastened the need to be more efficient and effective in deploying new technologies. Early Results are Positive.

Measurement 246

Measurement Metrics Data-driven Testing 246

Cloudera

OCTOBER 11, 2021

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. Cloudera Data Engineering within CDP provides : Fully managed Spark-on-Kubernetes service that hides the complexity running production DE workloads at scale.

Data Lake 89

Data Lake Cost-Benefit Data-driven Dashboards 89

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

AWS Big Data

APRIL 5, 2023

Showpad built new customer-facing embedded dashboards within Showpad eOSTM and migrated its legacy dashboards to Amazon QuickSight , a unified BI service providing modern interactive dashboards, natural language querying, paginated reports, machine learning (ML) insights, and embedded analytics at scale.

Dashboards 52

Dashboards Reporting Cost-Benefit Visualization 52

Jet Global

FEBRUARY 23, 2024

Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields. Data mapping helps standardize, visualize, and understand data across different systems and applications.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

Jet Global

MAY 1, 2023

These licensing terms are critical: Perpetual license vs subscription: Subscription is a pay-as-you-go model that provides flexibility as you evaluate a vendor. Pricing model: The pricing scale is dependent on several factors. Some cloud applications can even provide new benchmarks based on customer data.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52