Cloudera

DECEMBER 10, 2020

Some data warehousing solutions such as appliances and engineered systems have attempted to overcome these problems, but with limited success. . The providers, having never contributed to the core functionality, often don’t have extensive engineering knowledge of the functionality. Watch this video to get an overview of CDW. .

Data Warehouse 98

Data Warehouse Metadata Cost-Benefit Optimization 98

AWS Big Data

MARCH 2, 2023

It adds tables to compute engines including Spark, Trino, PrestoDB, Flink, and Hive using a high-performance table format that works just like a SQL table. Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. AWS Glue 3.0

Data Lake 117

Data Lake Data Processing Metadata Snapshot 117

IBM Big Data Hub

FEBRUARY 13, 2024

Generative AI uses advanced machine learning algorithms and techniques to analyze patterns and build statistical models. The quality of outputs depends heavily on training data, adjusting the model’s parameters and prompt engineering, so responsible data sourcing and bias mitigation are crucial. Garbage in, garbage out.

Enterprise 125

Enterprise Modeling Testing Data-driven 125

datapine

SEPTEMBER 29, 2022

Engineered to be the “Swiss Army Knife” of data development, these processes prepare your organization to face the challenges of digital age data, wherever and whenever they appear. Through the 5 pillars that we just presented above, we also covered some techniques and tips that should be followed to ensure a successful process.

Data Quality 241

Data Quality Metrics Data-driven Management 241

Ontotext

NOVEMBER 23, 2023

The RDF4J framework, and, by extension, GraphDB, offers a smart approach to this. At the lowest level, to the engine, there’s no difference between “data” and “SHACL shapes” — it’s all triples. Most data engineers are not Java developers. And besides Smaug, we have 1,000 other dragons.

Data Quality 69

Data Quality Reporting Metadata IT 69

Cloudera

AUGUST 25, 2020

Machine Learning and AI provide powerful predictive engines that rely on historical data to fit the models. I am optimistic that what we’ll see over the next 5 years is a mass acceleration and adoption of simulation techniques that complement the continuing innovation in the Machine Learning space. .

Prescriptive Analytics 69

Prescriptive Analytics Technology Machine Learning Risk 69

Juice Analytics

MARCH 11, 2021

Enabling users with features and capabilities to make them better in their job. Is the sales team in place to understand the product, the target audience and establish the sales framework for pushing the product? In a recent blog post , we described the differences between customer reporting and data products.

Predictive Modeling 128

Predictive Modeling Visualization Reporting Interactive 128

IBM Big Data Hub

MAY 26, 2023

Overall foundational pillars of the AWS security framework include the following: Compliance: AWS provides a range of compliance certifications and attestations that are relevant to the life sciences industry, including HIPAA, HITRUST and GxP. Data protection and privacy: Each AWS customer maintains ownership of their own data.

Consulting 80

Consulting Cost-Benefit Optimization Machine Learning 80

Cloudera

MAY 28, 2019

Gartner states that “By 2022, 75% of new end-user solutions leveraging machine learning (ML) and AI techniques will be built with commercial instead of open source platforms” ¹. Learn more about the Cloudera Data Science Workbench for the end-to-end ML workflow at our bi-weekly webinar series featuring live expert demos and Q&A.

Data Science 47

Data Science Machine Learning Data Strategy Cost-Benefit 47

O'Reilly on Data

OCTOBER 13, 2020

In traditional software engineering, precedent has been established for the transition of responsibility from development teams to maintenance, user operations, and site reliability teams. New features in an existing product often follow a similar progression.

Management 246

Management Metrics Machine Learning Modeling 246

Sisense

MARCH 18, 2020

They should do this by integrating mitigation techniques and policies, such as monitoring, patching, and vulnerability reporting. This danger looms greater than ever today thanks to machine learning techniques that are fundamentally changing the way software is made. Developing a better world.

Software 52

Software Machine Learning Internet of Things Risk 52

BizAcuity

APRIL 25, 2022

This blog has made a strong case for a Player lifetime value (LTV) framework. Keeping such numbers in mind, relying on old marketing techniques may not be a good idea. Can it be that popular without the recommendation features? The rules of the casino business have changed. from 2020 to 2027. Influencer Marketing.

Marketing 52

Marketing Strategy ROI Digital Transformation 52

The Unofficial Google Data Science Blog

APRIL 17, 2017

In this post, we recount how we approached the task, describing initial stakeholder needs, the business and engineering contexts in which the challenge arose, and theoretical and pragmatic choices we made to implement our solution. The demand for time series forecasting at Google grew rapidly along with the company over its first decade.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

DataKitchen

NOVEMBER 18, 2021

Yet, among all this, one area that hasn’t been studied is the data engineering role. We thought it would be interesting to look at how data engineers are doing under these circumstances. We surveyed 600 data engineers , including 100 managers, to understand how they are faring and feeling about the work that they are doing.

Testing 246

Testing Data Governance Measurement Software 246

O'Reilly on Data

MARCH 20, 2019

A recent flourish of posts and papers has outlined the broader topic, listed attack vectors and vulnerabilities, started to propose defensive solutions, and provided the necessary framework for this post. Inversion can also be an example of an “exploratory reverse-engineering” attack. they can train their own surrogate model.

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

datapine

APRIL 15, 2020

Business intelligence is moving away from the traditional engineering model: analysis, design, construction, testing, and implementation. To fully utilize agile business analytics, we will go through a basic agile framework in regards to BI implementation and management. Agile Business Intelligence & Analytics Methodology.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

datapine

NOVEMBER 15, 2022

4) Product Metrics Framework. As a PM, collecting information about your product performance, its features, the market adoption, etc., A common technique to break down the different types of key product metrics is using the AARRR framework , coined by venture capitalist Dave McClure. Table of Contents. is essential.

Metrics 141

Metrics KPI Measurement Dashboards 141

AWS Big Data

SEPTEMBER 13, 2023

Use case overview AnyCompany Travel and Hospitality wanted to build a data processing framework to seamlessly ingest and process data coming from operational databases (used by reservation and booking systems) in a data lake before applying machine learning (ML) techniques to provide a personalized experience to its users.

Data Lake 91

Data Lake Data Processing Metadata Snapshot 91

Andrew White

JANUARY 9, 2022

– In the webinar and Leadership Vision deck for Data and Analytics we called out AI engineering as a big trend. Can you remind where we can find the mentioned blog? So much hype compares electric vehicles to traditional engines. Can you provide a link to your blog, or will we get notice of your info posted through email?

Analytics 64

Analytics Measurement Modeling Data-driven 64

Cloudera

SEPTEMBER 23, 2022

In a recent blog, Cloudera Chief Technology Officer Ram Venkatesh described the evolution of a data lakehouse, as well as the benefits of using an open data lakehouse, especially the open Cloudera Data Platform (CDP). Analytical engines can be scaled up (or down) on demand, as per the requirements of your workload.

Data Governance 104

Data Governance Strategy Enterprise Cost-Benefit 104

Domino Data Lab

AUGUST 10, 2021

It’s a framework of processes, tools and protocols that empower the data science team at every step of the data science lifecycle (DSLC). XGBoost: is an open-source library that provides a regularizing gradient boosting framework for various programming languages. Types of Modeling Tools.

Modeling 64

Modeling IT Risk Management Deep Learning 64

Cloudera

NOVEMBER 13, 2020

This is part of our series of blog posts on recent enhancements to Impala. We’ll discuss the architecture and features of Impala that enable low latencies on small queries and share some practical tips on how to understand the performance of your queries. Impala is architected to be the Speed-of-Thought query engine for your data.

Optimization 143

Optimization Metadata Statistics Cost-Benefit 143

Laminar Security

SEPTEMBER 18, 2023

I’ve previously discussed the risk of shadow data (those data stores you didn’t even know you had), and the team specifically dove into how versioning (a feature used to protect from accidental deletion) can unintentionally create shadow data. In this case, the engineer may want to create a data dump to use for testing.

Risk 128

Risk Testing Data Governance Metadata 128

Alation

APRIL 14, 2022

Once migration is complete, it’s important that your data scientists and engineers have the tools to search, assemble, and manipulate data sources through the following techniques and tools. A technique to automate changes in iterative passes. A technique to automate changes in iterative passes. Parametrization.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Cloudera

OCTOBER 25, 2022

The cloud data lakehouse brings multiple processing engines (SQL, Spark, and others) and modern analytical tools (ML, data engineering, and business intelligence) together in a unified analytical environment. Consider this practice the most important function and foundation of your cloud security framework.

Data Processing 52

Data Processing Data Lake Cost-Benefit Risk 52

Cloudera

AUGUST 25, 2020

With only about 35% of machine learning models making into production in the enterprise ( IDC ), it’s no wonder that production machine learning has become one of the most important focus areas for data scientists and ML engineers alike. They can take many forms from Python-based rest APIs to R scripts to distributed frameworks like SparkML.

Machine Learning 74

Machine Learning Metrics Modeling Enterprise 74

Cloudera

SEPTEMBER 23, 2022

In a recent blog, Cloudera Chief Technology Officer Ram Venkatesh described the evolution of a data lakehouse, as well as the benefits of using an open data lakehouse, especially the open Cloudera Data Platform (CDP). Analytical engines can be scaled up (or down) on demand, as per the requirements of your workload.

Cost-Benefit 52

Cost-Benefit Software Data-driven Data Warehouse 52

Alation

FEBRUARY 20, 2020

By interacting with Tableau’s features to re-label data columns and build derived metrics, Tableau users were effectively documenting and modeling data on their own, often without realizing it. As teams work with Kylo, it captures the basic, technical metadata in the core source foundational framework.

Data Lake 52

Data Lake Metadata Structured Data Big Data 52

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. They’ve become “embedded institutions” in engineering. Introduction.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

JULY 28, 2021

Data scientists, analysts and ML engineers may move from one project to another, or they may leave the organization while new team members are brought on board. The Develop stage is where data scientists build and assess models based on a variety of different modeling techniques. Develop Stage. Deploy Stage. MLOps in the DSLC.

Data Science 59

Data Science Enterprise Modeling Experimentation 59

Cloudera

OCTOBER 17, 2017

In this blog, we provide a few examples that show how organizations put deep learning to work. Deep learning is a proven technique and a key driver for digital transformation. Users can download and experiment with the latest libraries and frameworks in customizable settings, and easily share projects with peers. Deeplearning4j.

Deep Learning 47

Deep Learning Data Science Machine Learning Modeling 47

Alation

OCTOBER 12, 2022

This blog post will break down how to use quantitative and qualitative data, as well as some common methods for analyzing the data to uncover quality insights. Segmentation is the technique of collecting data across two axes, such as details about the customer, and the specific goods or services purchase. What Is Quantitative Data?

Statistics 52

Statistics Sales Testing Marketing 52

Domino Data Lab

FEBRUARY 4, 2019

Also, it helps us differentiate between process that’s apt for data science work and process for software engineering, i.e., Agile, Kanban, and so on. Point #4 is where, IMO, many organizations tend to falter, then overcompensate by getting medieval via their data engineering teams—effectively erecting yet more silos.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

MARCH 6, 2019

This blog recaps Miner & Kasch ’s first Maryland Data Science Conference hosted at UMBC and dives into the Deep Learning on Imagery and Text talk presented by Florian Muellerklein and Bryan Wilkinson. Introduction. The conference was not without hiccups. You can see a complete list of talks see here. Deep Learning on Imagery and Text.

Deep Learning 55

Deep Learning Data Science Machine Learning Modeling 55

datapine

JULY 24, 2019

They can have a lot of different features, mainly being a customizable interface, a certain level of interactivity as well as the possibility to pull data in real-time from multiple sources. Analytical: This type of dashboard is engineered to provide detailed data analyses pertaining to data trends (the what, the why, the how).

Dashboards 148

Dashboards Business Intelligence Visualization KPI 148

Insight

MARCH 18, 2019

Bias in Machine Learning Algorithms (Bottom Photos Source: ProPublica ; Top Photos Source: Pexels.com) Biases in predictive modeling are a widespread issue Machine learning and AI applications are used across industries, from recommendation engines to self-driving cars and more. This rule is what we will use to judge the fairness of our work.

Machine Learning 77

Machine Learning Predictive Modeling Modeling Metrics 77

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

It also owns Google’s internal time series forecasting platform described in an earlier blog post. But even here, a greater concern is the time invested by data scientists (mostly at the development stage) in data analysis and cleaning, feature engineering, and model development. Our team does a lot of forecasting.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113