Knowledge Hut

DECEMBER 22, 2023

Linear Algebra Linear Algebra is a mathematical subject that is very useful in data science and machine learning. A dataset is frequently represented as a matrix. Statistics Statistics are at the heart of complex machine learning algorithms in data science, identifying and converting data patterns into actionable evidence.

Hadoop 98

Hadoop Deep Learning Data Science Machine Learning 98

DoorDash Engineering

APRIL 12, 2023

The Metrics Layer, also known as a Semantic Layer, is a critical component of the modern data stack that has recently received significant industry attention offers a powerful solution to the challenge of standardizing metric definitions. Lack of governance Our platform lacked governance policies for metric definitions.

SQL 82

SQL Metadata Raw Data Government 82

Knowledge Hut

JANUARY 30, 2024

In today's world, where data rules the roost, data extraction is the key to unlocking its hidden treasures. As someone deeply immersed in the world of data science, I know that raw data is the lifeblood of innovation, decision-making, and business progress. What is data extraction?

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

Towards Data Science

MARCH 9, 2023

Code implementations for ML pipelines: from raw data to predictions Photo by Rodion Kutsaiev on Unsplash Real-life machine learning involves a series of tasks to prepare the data before the magic predictions take place. Those are the features and their respective data types: Image 1 —Features and data types.

Machine Learning 72

Machine Learning Building Datasets Scala 72

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 Portfolio 52

Knowledge Hut

JUNE 28, 2023

By examining these factors, organizations can make informed decisions on which approach best suits their data analysis and decision-making needs. Parameter Data Mining Business Intelligence (BI) Definition The process of uncovering patterns, relationships, and insights from extensive datasets.

Data Mining 52

Data Mining Business Intelligence BI Datasets 52

dbt Developer Hub

SEPTEMBER 28, 2022

So let’s say that you have a business question, you have the raw data in your data warehouse , and you’ve got dbt up and running. You’re in the perfect position to get this curated dataset completed quickly! You’ve got three steps that stand between you and your finished curated dataset. Or are you?

Engineering 52

Engineering Raw Data Datasets BI 52

ProjectPro

FEBRUARY 8, 2023

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses. Why Use AWS Glue?

AWS 98

AWS Scala Metadata Data Lake 98

RandomTrees

DECEMBER 20, 2023

In this way, Edge AI facilitates the storage and computation of data, making it more accessible for users. Moreover, Edge AI combines AI algorithms with edge computing capabilities on local devices. Thus, data can get processed and analyzed without requiring connectivity and integration, allowing users to access data from various sources.

IT 52

IT Hospitality Healthcare Deep Learning 52

Ascend.io

DECEMBER 14, 2022

But in a world that favors the here and now, ETL processes lack in the area of providing analysts with new, fresh data. Low in Visibility End-users won’t be able to access all the data in the final destination, only the data that was transformed and loaded. This causes two issues.

Data Integration 52

Data Integration Raw Data Data Consolidation Data Warehouse 52

The Modern Data Company

MARCH 1, 2024

Each Data Product is designed for a specific purpose, equipped with the necessary data, transformations, and metadata. This modularity unlocks several key advantages: Reduced Complexity: Forget wrangling massive datasets. Data Products simplify access and processing, empowering faster decision-making.

Database-centric 40

Database-centric Data Lake Government Retail 40

Knowledge Hut

DECEMBER 26, 2023

If we look at history, the data that was generated earlier was primarily structured and small in its outlook. A simple usage of Business Intelligence (BI) would be enough to analyze such datasets. However, as we progressed, data became complicated, more unstructured, or, in most cases, semi-structured.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

Grouparoo

NOVEMBER 16, 2021

This is where data transformation can come to the rescue. What is Data Transformation Simply speaking, the data transformation definition is the process of converting data from diverse sources into a standard format that supports its analysis. One of the leaders in the space focused on data transforms is dbt.

Data Mining 52

Data Mining Raw Data ETL Tools Unstructured Data 52

Knowledge Hut

SEPTEMBER 27, 2023

The former empowers the data visualizations and analytical metrics from the raw data so businesses can use them to get insights and take action. Salesforce holds the capability to integrate with various data sources such as internal (customer records, interactions, leads, etc.),

BI 52

BI Datasets Data Security Data Analysis 52

Cloud Academy

JANUARY 27, 2022

A data engineer is an engineer who creates solutions from raw data. A data engineer develops, constructs, tests, and maintains data architectures. Let’s review some of the big picture concepts as well finer details about being a data engineer. Earlier we mentioned ETL or extract, transform, load.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Ascend.io

SEPTEMBER 14, 2023

We’ll explore its advantages, delve into its applications, and highlight why Python is increasingly becoming the first choice for data engineers worldwide. Why Python for Data Engineering? As the field of data engineering evolves, the need for a versatile, performant, and easily accessible language becomes paramount.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

Monte Carlo

FEBRUARY 15, 2023

Cleaning Bad data can derail an entire company, and the foundation of bad data is unclean data. Therefore it’s of immense importance that the data that enters a data warehouse needs to be cleaned. Data Transformation Raw data ingested into a data warehouse may not be suitable for analysis.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Meltano

OCTOBER 5, 2022

To reduce development time and increase data reliability, DataOps engineers automate manual processes, such as data extraction and testing. Managing the production of data pipelines. A DataOps engineer provides organizations with access to structured datasets and analytics they will further analyze and derive insights from.

Engineering 52

Engineering Raw Data Data Pipeline ETL Tools 52

ProjectPro

JUNE 9, 2021

As we proceed further into the blog, you will find some statistics on data engineering vs. data science jobs and data engineering vs. data science salary, along with an in-depth comparison between the two roles- data engineer vs. data scientist. vs. What does a Data Engineer do? What is Data Science?

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

ProjectPro

JANUARY 19, 2022

Data Engineering is gradually becoming a popular career option for young enthusiasts. Explore this page further and learn everything about data engineers to find the answer. We will cover it all, from its definition, skills, responsibilities to the significance of data engineer in an institution.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Airbnb Tech

JUNE 1, 2021

Specifically, we will showcase how we standardize dataset definitions through declarative configurations, explain how data versioning enables us to ensure cross-dataset consistency, and illustrate how we backfill data efficiently with zero downtime. Anyone can look up definitions without confusion.

Datasets 52

Datasets Pipeline-centric Metadata Data Science 52

U-Next

OCTOBER 27, 2022

You need to clean your data before you begin analyzing it so that you don’t end up with false conclusions or inaccurate results. . There are two main ways to clean your data: manual and automatic. Data cleaning and data transformation are processes that help transform data from its original state into a more useful format.

IT 40

IT Data Cleanse Datasets Bytes 40

Knowledge Hut

JUNE 20, 2023

Factors Data Engineer Machine Learning Definition Data engineers create, maintain, and optimize data infrastructure for data. In addition, they are responsible for developing pipelines that turn raw data into formats that data consumers can use easily.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

ProjectPro

FEBRUARY 16, 2023

Data engineers use SQL to modify any database and table structure and extract subsets of the data from the database for various business analytics use cases. For example, the data manipulation language (DML) and data definition language (DDL) allow engineers to collect and manipulate data scripts and design and modify data structures.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

Ascend.io

DECEMBER 11, 2023

This includes but is not limited to, job process sequencing, metadata synchronization, cataloging of data processing results, and managing the interdependencies of various data tasks. Not every team needs data orchestration. However, this approach quickly shows its limitations as data volume escalates.

Data Workflow 52

Data Workflow Data Pipeline Data Lake Data 52

ProjectPro

OCTOBER 18, 2021

Datasets like Google Local, Amazon product reviews, MovieLens, Goodreads, NES, Librarything are preferable for creating recommendation engines using machine learning models. 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

Monte Carlo

APRIL 24, 2023

As we saw in Part 2 of our series , the definition of “building” and “buying” can change based on what layer of the data stack we’re considering. Data ingestion When we think about the flow of data in a pipeline, data ingestion is where the data first enters our platform. When should you use dbt Core?

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

ProjectPro

MAY 31, 2021

A big data project is a data analysis project that uses machine learning algorithms and different data analytics techniques on a large dataset for several purposes, including predictive modeling and other advanced analytics applications. Kicking off a big data analytics project is always the most challenging part.

Big Data 52

Big Data Coding Project Hadoop 52

ProjectPro

NOVEMBER 30, 2021

If you are into Data Science or Big Data, you must be familiar with an ETL pipeline. This guide provides definitions, a step-by-step tutorial, and a few best practices to help you understand ETL pipelines and how they differ from data pipelines. How do we transform this data to get valuable insights from it?

Process 52

Process Data Pipeline Data Warehouse AWS 52

Monte Carlo

JANUARY 12, 2023

The first team is responsible for building the backend data infrastructure. The second team, data services , are more application or front-end oriented. They are building internal apps, UIs, calendars, and other interfaces to make our data as accessible as possible. Having this central layer unlocks data self-service.

Architecture 52

Architecture Building Data Warehouse BI 52

Monte Carlo

JUNE 23, 2022

But those are specialized use cases, and while the demand for streaming data poses new big data challenges on the horizon, today most organizations enjoy a technological moment in time where they can cost effectively access enough storage and compute to meet their organization’s needs without breaking a sweat.

Big Data 40

Big Data Management Machine Learning Data Warehouse 40

ProjectPro

JULY 26, 2021

Developed by the Google Brain Team, TensorFlow is an open-source platform that helps machine learning engineers and data scientists build models and deploy applications easily. With TensorFlow, getting started, building models, and debugging is made easy with access to high-level APIs like Keras.

Project 40

Project Deep Learning Medical Datasets 40

dbt Developer Hub

MAY 18, 2022

Applying sort and distribution configurations from dbt doesn’t affect how your raw data is sorted and distributed. Since dbt operates on top of raw data that’s already loaded into your warehouse, the following examples are geared towards optimizing your models created with dbt.

Raw Data 59

Raw Data Consulting Datasets IT 59

RandomTrees

NOVEMBER 29, 2023

Under any situation if a link gets repeatedly connected to its own hub more than once, it may possibly become a same as link and in a hierarchical relational definition it becomes a hierarchical link which is beyond the scope here and hubs can be interconnected only with the help of a link.

Data Warehouse 52

Data Warehouse Data Lake Database-centric Data Cleanse 52

ProjectPro

JULY 3, 2015

The maximum value of big data can be extracted by integrating the in-memory processing capabilities of SAP HANA (High Performance Analytic Appliance) and the ability of Hadoop to store large unstructured datasets. “With Big Data, you’re getting into streaming data and Hadoop.

Hadoop 40

Hadoop Big Data Data Solutions Unstructured Data 40

Knowledge Hut

OCTOBER 11, 2023

out of 5 stars (1,054 ratings) Book Info: Overview of what is covered in the book: The "Microsoft Power BI Cookbook" by Brett Powell, is a Power BI book that covers over 100 technical recipes, skills and use cases which guide you through the data visualization process. The book is divided into two parts.

BI 52

BI Business Intelligence Data Analysis Data Preparation 52

ProjectPro

AUGUST 16, 2021

Evolution of Machine Learning Applications in Finance : From Theory to Practice Music Recommendation System on KKbox Dataset The project aims at predicting if a user will listen to a song again in a period. KKbox provides a dataset for the project in user-song pairs and the first recorded listening time, along with song and user details.

Machine Learning 40

Machine Learning Coding Project Deep Learning 40