Timo Elliott

JANUARY 6, 2022

Like the proverbial man looking for his keys under the streetlight , when it comes to enterprise data, if you only look at where the light is already shining, you can end up missing a lot. The data you’ve collected and saved over the years isn’t free. Analyze your metadata. Real-time, cloud-based data ingestion and storage.

IT 135

IT Metadata Data-driven Data Governance 135

AWS Big Data

DECEMBER 4, 2024

SageMaker Lakehouse enables seamless data access directly in the new SageMaker Unified Studio and provides the flexibility to access and query your data with all Apache Iceberg-compatible tools on a single copy of analytics data. Having confidence in your data is key. The tools to transform your business are here.

Data Analytics 104

Data Analytics Analytics Data Lake Data Quality 104

DataRobot Blog

MARCH 16, 2023

Traditionally, developing appropriate data science code and interpreting the results to solve a use-case is manually done by data scientists. The integration allows you to generate intelligent data science code that reflects your use case. Data scientists still need to review and evaluate these results.

Data Science 98

Data Science Technology Data-driven Modeling 98

Rocket-Powered Data Science

JULY 19, 2023

That is not a totally clear separation and distinction, but it might help to clarify their different applications of data science. Data scientists work with business users to define and learn the rules by which precursor analytics models produce high-accuracy early warnings.

Data-driven 188

Data-driven Enterprise Analytics Machine Learning 188

Cloudera

MAY 19, 2021

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. RAPIDS brings the power of GPU compute to standard Data Science operations, be it exploratory data analysis, feature engineering or model building. Introduction.

Machine Learning 137

Machine Learning Data Science Data Lake Modeling 137

Data Virtualization

JULY 28, 2023

Reading Time: 2 minutes As the volume, variety, and velocity of data continue to surge, organizations still struggle to gain meaningful insights. This is where active metadata comes in. Listen to “Why is Active Metadata Management Essential?” What is Active Metadata? ” on Spreaker.

Metadata 52

Metadata Data Integration Management Data Science 52

Alation

FEBRUARY 13, 2020

In an earlier blog, I defined a data catalog as “a collection of metadata, combined with data management and search tools, that helps analysts and other data users to find the data that they need, serves as an inventory of available data, and provides information to evaluate fitness data for intended uses.”.

Metadata 52

Metadata Management Data Lake Data Governance 52

AWS Big Data

JUNE 7, 2023

This is a guest blog post co-written with Sumesh M R from Cargotec and Tero Karttunen from Knowit Finland. They chose AWS Glue as their preferred data integration tool due to its serverless nature, low maintenance, ability to control compute resources in advance, and scale when needed.

Metadata 59

Metadata Data Lake Machine Learning Big Data 59

Cloudera

NOVEMBER 13, 2020

This is part of our series of blog posts on recent enhancements to Impala. Apache Impala is synonymous with high-performance processing of extremely large datasets, but what if our data isn’t huge? Data science experiment result and performance analysis, for example, calculating model lift. Metadata Caching.

Optimization 143

Optimization Metadata Statistics Cost-Benefit 143

DataKitchen

JANUARY 5, 2021

Execution of this mission requires the contribution of several groups: data center/IT, data engineering, data science, data visualization, and data governance. Each of the roles mentioned above views the world through a preferred set of tools: Data Center/IT – Servers, storage, software.

Testing 147

Testing Data Governance Metadata Visualization 147

AWS Big Data

JUNE 10, 2024

In this blog post, we dive into different data aspects and how Cloudinary breaks the two concerns of vendor locking and cost efficient data analytics by using Apache Iceberg, Amazon Simple Storage Service (Amazon S3 ), Amazon Athena , Amazon EMR , and AWS Glue. This concept makes Iceberg extremely versatile. SparkActions.get().expireSnapshots(iceTable).expireOlderThan(TimeUnit.DAYS.toMillis(7)).execute()

Data Lake 126

Data Lake Metadata Snapshot Analytics 126

Cloudera

SEPTEMBER 9, 2022

Cloudera has been supporting data lakehouse use cases for many years now, using open source engines on open data and table formats, allowing for easy use of data engineering, data science, data warehousing, and machine learning on the same data, on premises, or in any cloud.

Metadata 102

Metadata Machine Learning Unstructured Data Data Lake 102

Cloudera

AUGUST 17, 2022

CDP One democratizes insights so companies can achieve faster time to business insights to create data-driven innovation. It is a simple, yet powerful, cloud service that will accelerate data science programs with built-in enterprise security and machine learning (ML). Accelerated Data Science at CWT.

Analytics 116

Analytics Data-driven Machine Learning Data Science 116

IBM Big Data Hub

OCTOBER 4, 2022

This includes capturing of the metadata, tracking provenance and documenting the model lifecycle. It drives a complete governance solution without the excessive costs of switching from your current data science platform. The post AI Governance: Break open the black box appeared first on Journey to AI Blog.

Metadata 97

Metadata Risk Management Risk Experimentation 97

DataRobot Blog

SEPTEMBER 1, 2022

These and many other questions are now on top of the agenda of every data science team. To quantify how well your models are doing, DataRobot provides you with a comprehensive set of data science metrics — from the standards (Log Loss, RMSE) to the more specific (SMAPE, Tweedie Deviance). Learn More About DataRobot MLOps.

Metrics 145

Metrics Statistics Modeling Data Science 145

Cloudera

SEPTEMBER 28, 2020

The data lifecycle model ingests data using Kafka, enriches that data with Spark-based batch process, performs deep data analytics using Hive and Impala, and finally uses that data for data science using Cloudera Data Science Workbench to get deep insights. on roadmap).

Testing 132

Testing Metadata Risk Data Science 132

Cloudera

OCTOBER 11, 2022

This leads to the obvious question – how do you do data at scale ? Al needs machine learning (ML), ML needs data science. Data science needs analytics. And they all need lots of data. And that data is likely in clouds, in data centers and at the edge.

Data Science 94

Data Science Snapshot Data Warehouse Metadata 94

Domino Data Lab

JANUARY 21, 2021

This blog explores the challenges associated with doing such work manually, discusses the benefits of using Pandas Profiling software to automate and standardize the process, and touches on the limitations of such tools in their ability to completely subsume the core tasks required of data science professionals and statistical researchers.

Statistics 106

Statistics Unstructured Data Data Science Visualization 106

Domino Data Lab

JUNE 13, 2019

Domino Data Science Field Notes provide highlights of data science research, trends, techniques, and more, that support data scientists and data science leaders accelerate their work or careers. . “In the same spirit as [Recht et al., ” They also were able to.

Testing 83

Testing Data Science Experimentation Metadata 83

Cloudera

MAY 22, 2018

Cloudera Data Science Workbench (CDSW) makes secure, collaborative data science at scale a reality for the enterprise and accelerates the delivery of new data products. save the built model container, along with metadata like who built or deployed it. Cloudera Data Science Workbench 1.4.x

Data Science 41

Data Science Snapshot Machine Learning Data Warehouse 41

datapine

JANUARY 31, 2022

Although the oil company has been producing massive amounts of data for a long time, with the rise of new cloud-based technologies and data becoming more and more relevant in business contexts, they needed a way to manage their information at an enterprise level and keep up with the new skills in the data industry.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Cloudera

AUGUST 24, 2022

It unifies self-service data science and data engineering in a single, portable service as part of an enterprise data cloud for multi-function analytics on data anywhere. As for the container labels, you can find more information about this in Metadata for Customer ML Runtime , within Cloudera documentation. .

Machine Learning 83

Machine Learning Metadata Testing Data Science 83

IBM Big Data Hub

OCTOBER 4, 2023

Typically, on their own, data warehouses can be restricted by high storage costs that limit AI and ML model collaboration and deployments, while data lakes can result in low-performing data science workloads. New insights and relationships are found in this combination. All of this supports the use of AI.

Data Lake 77

Data Lake Metadata Data Warehouse Cost-Benefit 77

AWS Big Data

MAY 4, 2023

Amazon’s Open Data Sponsorship Program allows organizations to host free of charge on AWS. Over the last decade, we’ve seen a surge in data science frameworks coming to fruition, along with mass adoption by the data science community. Data scientists have access to the Jupyter notebook hosted on SageMaker.

Data Processing 122

Data Processing Metadata Informatics Interactive 122

Cloudera

DECEMBER 15, 2022

With in-place table migration, you can rapidly convert to Iceberg tables since there is no need to regenerate data files. Only metadata will be regenerated. Newly generated metadata will then point to source data files as illustrated in the diagram below. . Data quality using table rollback. Metadata management .

Metadata 80

Metadata Data Warehouse Snapshot Machine Learning 80

IBM Big Data Hub

APRIL 27, 2022

Gartner defines a data fabric as “a design concept that serves as an integrated layer of data and connecting processes. The data fabric architectural approach can simplify data access in an organization and facilitate self-service data consumption at scale. 2 “Exposing The Data Mesh Blind Side ” Forrester.

Management 79

Management Metadata Data Architecture Data Lake 79

Cloudera

AUGUST 30, 2022

Data Mesh: A type of data platform architecture that embraces the ubiquity of data in the enterprise by leveraging a domain-oriented, self-serve design. A data mesh supports distributed, domain-specific data consumers and views data as a product, with each domain handling its own data pipelines.

Data Architecture 79

Data Architecture Data Lake Data Warehouse Metadata 79

Cloudera

APRIL 1, 2024

Cloudera, a leader in big data analytics, provides a unified Data Platform for data management, AI, and analytics. Our customers run some of the world’s most innovative, largest, and most demanding data science, data engineering, analytics, and AI use cases, including PB-size generative AI workloads.

Unstructured Data 105

Unstructured Data Cost-Benefit Metadata Machine Learning 105

Cloudera

NOVEMBER 8, 2022

Ozone is also highly available — the Ozone metadata is replicated by Apache Ratis, an implementation of the Raft consensus algorithm for high-performance replication. Since Ozone supports both Hadoop FileSystem interface and Amazon S3 interface, frameworks like Apache Spark, YARN, Hive, and Impala can automatically use Ozone to store data.

Metadata 84

Metadata Data-driven Management Optimization 84

O'Reilly on Data

MARCH 20, 2019

Especially in today’s overheated and turnover-prone data “science” market.) Along with model documentation, all modeling artifacts, source code, and associated metadata need to be managed, versioned, and audited for security like the valuable commercial assets they are.

Modeling 278

Modeling Machine Learning Predictive Modeling Consulting 278

Cloudera

JUNE 7, 2022

Only Cloudera has the power to span multi-cloud and on-premises with a hybrid data platform. We deliver cloud-native data analytics across the full data lifecycle – data distribution, data engineering, data warehousing, transactional data, streaming data, data science, and machine learning – that’s portable across infrastructures.

IT 112

IT Data Architecture Unstructured Data Big Data 112

IBM Big Data Hub

MAY 9, 2023

It drives an AI governance solution without the excessive costs of switching from your current data science platform. The resulting automation drives scalability and accountability by capturing model development time and metadata, offering post-deployment model monitoring, and allowing for customized workflows.

Metadata 71

Metadata Risk Experimentation Dashboards 71

Cloudera

JUNE 16, 2022

CSP was recently recognized as a leader in the 2022 GigaOm Radar for Streaming Data Platforms report. The DevOps/app dev team wants to know how data flows between such entities and understand the key performance metrics (KPMs) of these entities. She is a smart data analyst and former DBA working at a planet-scale manufacturing company.

Data Lake 88

Data Lake Manufacturing Metadata Dashboards 88

IBM Big Data Hub

JANUARY 17, 2023

These new technologies and approaches, along with the desire to reduce data duplication and complex ETL pipelines, have resulted in a new architectural data platform approach known as the data lakehouse – offering the flexibility of a data lake with the performance and structure of a data warehouse.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

Cloudera

OCTOBER 11, 2021

The platform converges data cataloging, data ingestion, data profiling, data tagging, data discovery, and data exploration into a unified platform, driven by metadata. Modak Nabu automates repetitive tasks in the data preparation process and thus accelerates the data preparation by 4x.

Data Lake 95

Data Lake Cost-Benefit Data-driven Dashboards 95

Alation

FEBRUARY 20, 2020

Prior to the Big Data revolution, companies were inward-looking in terms of data. During this time, data-centric environments like data warehouses dealt only with data created within the enterprise. THE NEED FOR METADATA TOOLS. Given the characteristics of data acquisition, how should it be handled?

Metadata 52

Metadata IT Data Governance Data Warehouse 52

Cloudera

OCTOBER 7, 2022

To date, dbt was only available on proprietary cloud data warehouses, with very little interoperability between different engines. For example, transformations performed in one engine are not visible across other engines because there was no common storage or metadata store. CDP Private Cloud via Cloudera Data Science Workbench.

Data Warehouse 81

Data Warehouse Data Transformation Machine Learning Data Lake 81