Knowledge Hut

JANUARY 24, 2024

Join me on this captivating expedition as we peel back the curtain, revealing the intricacies that define "A Day in the Life of a Data Scientist." This blog offers an exclusive glimpse into the daily rituals, challenges, and moments of triumph that punctuate the professional journey of a data scientist.

Database-centric 52

Database-centric Data Science Machine Learning Datasets 52

Databand.ai

AUGUST 30, 2023

Data Testing Tools: Key Capabilities and 6 Tools You Should Know Helen Soloveichik August 30, 2023 What Are Data Testing Tools? Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing, and maintaining data quality.

Data Cleanse 52

Data Cleanse Data Validation Data Pipeline Datasets 52

Knowledge Hut

NOVEMBER 27, 2023

In fact, you reading this blog is also being recorded as an instance of data in some digital storage. In 2018, the world produced 33 Zettabytes (ZB) of data, which is equivalent to 33 trillion Gigabytes (GB). Learn how to import data, to visualize data using libraries like Matplotlib and Seaborn.

Data Science 97

Data Science Healthcare Machine Learning Telecommunication 97

Striim

NOVEMBER 17, 2023

Real-time data processing in the world of machine learning allows data scientists and engineers to focus on model development and monitoring. Striim’s strength lies in its capacity to connect to over 150 data sources, enabling real-time data acquisition from virtually any location and simplifying data transformations.

Machine Learning 52

Machine Learning Data Process PostgreSQL Process 52

Knowledge Hut

JULY 28, 2023

Data Labeling is the process of assigning meaningful tags or annotations to raw data, typically in the form of text, images, audio, or video. These labels provide context and meaning to the data, enabling machine learning algorithms to learn and make predictions. What is Data Labeling for Machine Learning?

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Machine Learning Process Datasets Raw Data 52

ProjectPro

JUNE 9, 2021

This blog on Data Science vs. Data Engineering presents a detailed comparison between the two domains. vs. What does a Data Engineer do? Are you a Data Scientist or a Data Engineer? Is data engineering more important than data science?

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Lyft Engineering

APRIL 25, 2023

Building a large scale unsupervised model anomaly detection system — Part 2 Building ML Models with Observability at Scale By Rajeev Prabhakar , Han Wang , Anindya Saha Photo by Octavian Rosca on Unsplash In our previous blog we discussed the different challenges we faced for model monitoring and our strategy for addressing some of these problems.

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Systems Building Machine Learning Datasets 75

Knowledge Hut

NOVEMBER 27, 2023

A data scientist’s job needs loads of exploratory data research and analysis on a daily basis with the help of various tools like Python, SQL, R, and Matlab. This role is an amalgamation of art and science that requires a good amount of prototyping, programming and mocking up of data to obtain novel outcomes.

Data Science 52

Data Science Datasets Machine Learning Algorithm 52

Knowledge Hut

MARCH 18, 2024

Consequently, the demand for professionals in both data analytics and project management remains exceedingly high in today's business landscape. As a data analyst, I focus on diving into data to draw out valuable insights, while project managers are all about leading and coordinating projects.

Data Analytics 52

Data Analytics Project Management Programming Language 52

DataKitchen

MAY 4, 2023

The data industry is now adopting similar principles, such as data testing instead of test-driven development, data observability instead of observability, and functional data engineering instead of functional programming. Chris talks about the idea of a ‘domain’ as a principle of Data Mesh.

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Raw Data Data Datasets Metadata 52

Cloudera

SEPTEMBER 13, 2018

Feature engineering, or the process of transforming raw data into a chosen representation of the underlying problem to be solved, is the data science process most ripe for automation. Before any machine learning can be applied to a business problem, data scientists need to define the problem itself.

Machine Learning 40

Machine Learning Engineering Raw Data Data Science 40

Workfall

JULY 4, 2023

Reading Time: 8 minutes In the world of data engineering, a mighty tool called DBT (Data Build Tool) comes to the rescue of modern data workflows. Imagine a team of skilled data engineers on an exciting quest to transform raw data into a treasure trove of insights. Happy DBT-ing!

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Data Warehouse SQL PostgreSQL Database 69

Knowledge Hut

JANUARY 23, 2024

It's like the hidden dance partner of algorithms and data, creating an awesome symphony known as "Math and Data Science." " So, get ready for a fun ride in this blog as we explore the fascinating world of math in data science. Here are key areas of mathematics that data scientists must be familiar with: A.

Data Science 52

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Cloudera

JANUARY 20, 2021

The missing chapter is not about point solutions or the maturity journey of use cases, the missing chapter is about the data, it’s always been about the data, and most importantly the journey data weaves from edge to artificial intelligence insight. . Data Collection Using Cloudera Data Platform.

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DareData

JULY 5, 2023

If you work at a relatively large company, you've seen this cycle happening many times: Analytics team wants to use unstructured data on their models or analysis. For example, an industrial analytics team wants to use the logs from raw data. Understanding the Architecture No company is alike and no infrastructure will be alike.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

Databand.ai

JUNE 14, 2023

Ryan Yackel June 14, 2023 Understanding Data Quality Testing Data quality testing refers to the evaluation and validation of a dataset’s accuracy, consistency, completeness, and reliability. Risk mitigation: Data errors can result in expensive mistakes or even legal issues.

Amazon Web Services 52

Amazon Web Services Datasets High Quality Data ETL Tools 52

RandomTrees

DECEMBER 20, 2023

Once trained, the model begins to answer real-world questions through an “inference engine.” The training process gets improved by uploading a relevant subset of raw data when uploaded to the cloud. A federated learning system updates AI training locally on the edge device.

IT 52

IT Hospitality Healthcare Deep Learning 52

Picnic Engineering

JULY 24, 2023

In this blog post, we’ll discuss how we teach transformers to distinguish between products like a potato and a banana, thereby enhancing future demand prediction. By converting raw data into valuable information, transformer models could significantly contribute to sustainability. Then you are in the right place!

Datasets 52

Datasets Architecture Utilities Raw Data 52

AltexSoft

AUGUST 25, 2021

Internet search engines are wonderfully helpful when auto-filling our queries, language translation has never been more seamless and correct, and advanced grammar checks save our reputation when we’re sending emails. You can’t simply feed the system your whole dataset of emails and expect it to understand what you want from it.

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Process Deep Learning Datasets Machine Learning 139

Cloudera

DECEMBER 17, 2020

First and foremost, we designed the Cloudera Data Platform (CDP) to optimize every step of what’s required to go from raw data to AI use cases. CDP enables a fully integrated and seamless ML lifecycle — from data pipelines to production and everything in between. Laurence Goasduff, Gartner.

Machine Learning 80

Machine Learning Data Science Data Pipeline Raw Data 80

Rockset

AUGUST 2, 2023

The Windward Maritime AI platform Lastly, Windward wanted to move their entire platform from batch-based data infrastructure to streaming. In this blog, we’ll describe the new data platform for Windward and how it is API first, enables rapid product iteration and is architected for real-time, streaming data.

Database-centric 52

Database-centric PostgreSQL Transportation Insurance 52

ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. A pipeline may include filtering, normalizing, and data consolidation to provide desired data. ETL is the acronym for Extract, Transform, and Load.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

ProjectPro

JULY 12, 2021

You can consider many other high-paying career options as a data enthusiast. This blog will take you through a relatively new career title in the data industry — AI Engineer. Table of Contents Why do you need to become an AI Engineer: Are AI Engineers in Demand? What is an AI Engineer?

Engineering 40

Engineering Deep Learning Software Engineer Software Engineering 40

ProjectPro

OCTOBER 18, 2021

Recommendation engines are popular in media, entertainment, and shopping. All modern apps come with a recommendation engine that suggests users for more engagement. They have a well-researched collection of data such as ratings, reviews, timestamps, price, category information, customer likes, and dislikes.

Machine Learning 52

Machine Learning Project Datasets Algorithm 52

Knowledge Hut

DECEMBER 26, 2023

Data Engineering is typically a software engineering role that focuses deeply on data – namely, data workflows, data pipelines, and the ETL (Extract, Transform, Load) process. What is Data Science? What are the roles and responsibilities of a Data Engineer? And many more.

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Data Engineering Data Engineer Engineering Pipeline-centric 98

Rockset

JANUARY 10, 2019

This post outlines how to use SQL for querying and joining raw data sets like nested JSON and CSV - for enabling fast, interactive data science. Data scientists and analysts deal with complex data. Much of what they analyze could be third-party data, over which there is little control.

Data Science 40

Data Science SQL Relational Database Raw Data 40

DataKitchen

OCTOBER 2, 2023

This type of Data Journey provides a continuous monitoring framework that can be augmented by data quality checks (such as those automatically generated by DataKitchen’s TestGen product ), ensuring the quality of datasets and tables. The Hub Data Journey provides the raw data and adds value through a ‘contract.

Data Ingestion 40

Data Ingestion Data Government Datasets 40

DataKitchen

MARCH 16, 2023

Under Velocity, the Mean Time to Deliver Data metric measures the time it takes to deliver data. The Active Data Ratio metric determines the percentage of datasets that deliver value. The Data Change Request Ratio metric measures the rate of business demand for data. Data trust is imperative.

Data Analytics 52

Data Analytics Manufacturing Data Process 52

Monte Carlo

JULY 7, 2022

Data contracts have since became one of the most discussed topics in data engineering. For posterity, we have preserved Barr’s forward that examines what was then a very nascent trend, but we have also added an updated data contract FAQ as an addendum. Forward: A Data Engineering Movement Afoot?

Data Warehouse 52

Data Warehouse Software Engineer Software Engineering Data 52

Ascend.io

APRIL 5, 2023

One recent example is a relatively new role that sits one step “up the stack” from data engineers: the analytics engineer. What is an analytics engineer? Analytics engineers are specialists that bridge the gap between highly technical data engineering and outcome-driven analytics.

Engineering 52

Engineering Raw Data BI Software Engineer 52

AltexSoft

JANUARY 26, 2022

In recent years, it’s getting more common to see organizations looking for a mysterious analytics engineer. As you may guess from the name, this role sits somewhere in the middle of a data analyst and data engineer, but it’s really neither one nor the other. What an analytics engineer is. Sounds interesting, right?

Engineering 59

Engineering Software Engineer Software Engineering Data Warehouse 59

Maxime Beauchemin

AUGUST 28, 2017

This post follows up on The Rise of the Data Engineer , a recent post that was an attempt at defining data engineering and described how this new role relates to historical and modern roles in the data space. Understanding and exposing the adversity that the role is facing is a first step towards finding solutions.

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Data Engineering Data Engineer Engineering Software Engineer 174

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. Get the Dataset.

Machine Learning 136

Machine Learning Datasets Data Science Raw Data 136

Confluent

SEPTEMBER 26, 2019

Leveraging Rockset , a scalable SQL search and analytics engine based on RocksDB , and in conjunction with BI and analytics tools, we’ll examine a solution that performs interactive, real-time analytics on top of Apache Kafka and also show a live monitoring dashboard example with Redash. Joining with other datasets.

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Kafka BI SQL Datasets 21

ProjectPro

AUGUST 24, 2021

Nevertheless, that is not the only job in the data world. Data professionals who work with raw data like data engineers, data analysts, machine learning scientists , and machine learning engineers also play a crucial role in any data science project.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Databand.ai

JULY 6, 2023

Data pipelines are a series of data processing steps that enable the flow and transformation of raw data into valuable insights for businesses. Automating the processes of data engineering can ensure dependable and effective delivery of high-quality information to support decision making.

Data Pipeline 52

Data Pipeline Data Science Raw Data Media 52

Maxime Beauchemin

JANUARY 7, 2018

In this post, we’ll explore how applying the functional programming paradigm to data engineering can bring a lot of clarity to the process. This post distills fragments of wisdom accumulated while working at Yahoo, Facebook, Airbnb and Lyft, with the perspective of well over a decade of data warehousing and data engineering experience.

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Data Engineering Data Engineer Data Process Process 183