DataRobot Blog

MARCH 10, 2022

By enabling data scientists to rapidly iterate through model development, validation, and deployment, DataRobot provides the tools to blitz through steps four and five of the machine learning lifecycle with AutoML and Auto Time-Series capabilities. Train, Compare, Rank, Validate, and Select Models for Production.

Manufacturing 52

Manufacturing IoT Machine Learning Forecasting 52

DataKitchen

OCTOBER 5, 2021

The data organization wants to run the Value Pipeline as robustly as a six sigma factory, and it must be able to implement and deploy process improvements as rapidly as a Silicon Valley start-up. The data engineer builds data transformations. Their product is the data.

Testing 152

Testing Dashboards Measurement Experimentation 152

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. Additionally, you can configure OpenSearch Ingestion to apply data transformations before delivery.

Dashboards 63

Dashboards Testing Metrics Optimization 63

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

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 326

Machine Learning Predictive Modeling Software Modeling 326

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

AWS Big Data

OCTOBER 11, 2023

In this post, we discuss ways to modernize your legacy, on-premises, real-time analytics architecture to build serverless data analytics solutions on AWS using Amazon Managed Service for Apache Flink. Near-real-time streaming analytics captures the value of operational data and metrics to provide new insights to create business opportunities.

Management 105

Management Metadata Analytics Dashboards 105

Ontotext

APRIL 4, 2019

Milena Yankova : We help the BBC and the Financial Times to model the knowledge available in various documents so they can manage it. They have different metrics for judging whether some content is interesting or not. Economy.bg: You work with media companies such as the BBC and the Financial Times. What exactly do you do for them?

Recreation/Entertainment 76

Recreation/Entertainment Testing Enterprise Knowledge Discovery 76

CIO Business Intelligence

JUNE 23, 2022

Poor data quality is cited as the major reason initiatives fail to achieve their expected value – up to 60% of business initiatives fail because of data quality issues. Data quality becomes an even more pressing issue as organizations move toward AI/ML-enabled decision-making. Supply chain complexity.

Data Quality 97

Data Quality Key Performance Indicator Metrics KPI 97

Alation

OCTOBER 18, 2022

Alation is pleased to be named a dbt Metrics Partner and to announce the start of a partnership with dbt, which will bring dbt data into the Alation data catalog. In the modern data stack, dbt is a key tool to make data ready for analysis. Data Transformation in the Modern Data Stack.

Metadata 52

Metadata Metrics Recreation/Entertainment Data Quality 52

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

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 96

Dashboards Analytics Metadata Data Warehouse 96

Sisense

AUGUST 6, 2020

An obvious mechanical answer is: use relevance as a metric. Another important method is to benchmark existing metrics. Know the limitations of your existing dataset and answer these questions: What categories of data are there? What data transformations are needed from your data scientists to prepare the data?

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

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

Alation

OCTOBER 28, 2021

Healthcare is changing, and it all comes down to data. Leaders in healthcare seek to improve patient outcomes, meet changing business models (including value-based care ), and ensure compliance while creating better experiences. Data & analytics represents a major opportunity to tackle these challenges.

Data Governance 52

Data Governance Measurement Metrics Data Quality 52

AWS Big Data

JULY 20, 2023

This data is then used by various applications for streaming analytics, business intelligence, and reporting. Amazon SageMaker is used to build, train, and deploy a range of ML models. This ensures that the data is suitable for training purposes. Additionally, SageMaker training jobs are employed for training the models.

Data Lake 88

Data Lake Analytics Snapshot Optimization 88

Cloudera

JUNE 17, 2022

Figure 3: A scalable, robust syslog data pipeline powered by CDF-PC’s flow deployments with Inbound Connections. Kafka REST Proxy for streaming data. Apache Kafka is a popular open-source messaging platform that heavily relies on the push model to ingest data from producers into topics.

Cost-Benefit 97

Cost-Benefit IoT Data Warehouse Manufacturing 97

DataKitchen

OCTOBER 25, 2022

It’s because it’s a hard thing to accomplish when there are so many teams, locales, data sources, pipelines, dependencies, data transformations, models, visualizations, tests, internal customers, and external customers. That data then fills several database tables. It’s not just a fear of change.

Testing 130

Testing Data-driven Visualization Dashboards 130

Ontotext

DECEMBER 9, 2020

Are you having difficulty joining your knowledge graph APIs with other data sources? Maybe you spend an inordinate amount of time and effort managing operational concerns, deployments, monitoring, metrics and log collation? This leads to lots of small data fetches to/from GraphDB over the network.

Modeling 130

Modeling Management Optimization Metrics 130

AWS Big Data

NOVEMBER 29, 2023

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt is predominantly used by data warehouses (such as Amazon Redshift ) customers who are looking to keep their data transform logic separate from storage and engine.

Data Lake 103

Data Lake Management Metrics Data Warehouse 103

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. Dave Thibault is a Sr.

Cost-Benefit 55

Cost-Benefit Informatics Optimization Management 55

AWS Big Data

MARCH 3, 2023

It has been well published since the State of DevOps 2019 DORA Metrics were published that with DevOps, companies can deploy software 208 times more often and 106 times faster, recover from incidents 2,604 times faster, and release 7 times fewer defects. Fixed-size data files avoid further latency due to unbound file sizes.

Software 52

Software Data Lake Testing Cost-Benefit 52

DataKitchen

APRIL 26, 2021

Furthermore, the introduction of AI and ML models hastened the need to be more efficient and effective in deploying new technologies. Similarly, Workiva was driven to DataOps due to an increased need for analytics agility to meet a range of organizational needs, such as real-time dashboard updates or ML model training and monitoring.

Measurement 246

Measurement Metrics Data-driven Testing 246

Occam's Razor

OCTOBER 19, 2010

Mongoose Metrics ~ ifbyphone. I know Mongoose Metrics a bit more and have been impressed with their solution and evolution over the last couple of years. Twitter to me is a proxy of how data collection is changing and what the future of relevant metrics might look like. Mongoose Metrics. AnalyzeWords. LivePerson.

Analytics 135

Analytics Testing Measurement Optimization 135

AWS Big Data

NOVEMBER 7, 2023

Second, organizations still need transformations like cleansing, deduplication, and combining datasets for analysis and machine learning (ML). For these, AWS Glue provides fast, scalable data transformation. They can now analyze business metrics in near-real time and make data-driven decisions faster than ever before.

Dashboards 73

Dashboards Data-driven Data Integration Data Lake 73

CIO Business Intelligence

APRIL 30, 2024

This feature provides users the ability to explore metrics with natural language. Tableau Pulse will then send insights for that metric directly to the executive’s preferred communications platform: Slack, email, mobile device, etc. Metrics Bootstrapping. Metric Goals.

Analytics 77

Analytics Metrics Visualization Dashboards 77

AWS Big Data

SEPTEMBER 19, 2023

We also need to be able to add new fields and metrics to the risk profiles as our team identifies new attacks, without needing engineering intervention or complex deployments. Our data scientist team can access the dataset and perform ML model training and batch inferencing using Amazon SageMaker.

Analytics 79

Analytics Risk Big Data Machine Learning 79

Alation

NOVEMBER 22, 2022

This is why public agencies are increasingly turning to an active governance model, which promotes data visibility alongside in-workflow guidance to ensure secure, compliant usage. An active data governance framework includes: Assigning data stewards. Standardizing data formats. Gain visibility into data history.

Data Governance 52

Data Governance Metadata Data-driven Unstructured Data 52

DataKitchen

JANUARY 23, 2023

On the other hand, DataOps Observability refers to understanding the state and behavior of data as it flows through systems. It allows organizations to see how data is being used, where it is coming from, and how it is being transformed. All of those things come together with an overlay of automated tests.

Testing 130

Testing Data Governance Data Quality Data-driven 130

Jet Global

MAY 28, 2019

A data warehouse stores transactional level details and serves the broader reporting and analytical needs of an organization – creating one source of truth for building semantic models or serving structured, simplified and harmonized data to tools like Power BI, Excel or even SSRS. Download Now.

Data Warehouse 85

Data Warehouse Reporting Business Intelligence Sales 85

AWS Big Data

DECEMBER 21, 2023

For the downstream consumption by all departments across the organization, smava’s Data Platform team prepares curated data products following the extract, load, and transform (ELT) pattern. The Raw Vault describes objects loaded directly from the data sources and represents a copy of the landing stage in the data lake.

Data Lake 89

Data Lake Data Warehouse Data-driven B2B 89

Birst BI

FEBRUARY 25, 2020

The benefits of Birst’s cloud-native analytics platform powered by Snowflake’s world-class cloud data warehouse are numerous, especially as cloud becomes the first choice for enterprise data transformation initiatives.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Architecture Enterprise 52

Jet Global

MAY 1, 2023

As a result, end users can better view shared metrics (backed by accurate data), which ultimately drives performance. 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. Perpetual, by contrast, is paid up front.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

MAY 9, 2024

Data Extraction : The process of gathering data from disparate sources, each of which may have its own schema defining the structure and format of the data and making it available for processing. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52