Data Engineering Podcast

JUNE 26, 2022

Summary The most complicated part of data engineering is the effort involved in making the raw data fit into the narrative of the business. Random data doesn’t do it — and production data is not safe (or legal) for developers to use. How have evolutions in the data landscape shifted the conversation around MDM?

Data Management 100

Data Management Management MongoDB Scala 100

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

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

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. We will also dive deep into our design and implementation processes and the lessons we learnt.

SQL 82

SQL Metadata Raw Data Government 82

Knowledge Hut

JUNE 28, 2023

The answer lies in the strategic utilization of business intelligence for data mining (BI). Although these terms are sometimes used interchangeably, they carry distinct meanings and play different roles in this process. Process of analyzing, collecting, and presenting data to support decision-making.

Data Mining 52

Data Mining Business Intelligence BI Datasets 52

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

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

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. If we look at history, the data that was generated earlier was primarily structured and small in its outlook.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

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

RandomTrees

DECEMBER 20, 2023

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. Enhanced data security. Improved reliability/autonomous technology.

IT 52

IT Hospitality Healthcare Deep Learning 52

AltexSoft

MAY 12, 2022

Our brains are constantly processing sounds to give us important information about our environment. Particularly, we’ll explain how to obtain audio data, prepare it for analysis, and choose the right ML model to achieve the highest prediction accuracy. To make audio understandable for computers, data must undergo a transformation.

Machine Learning 122

Machine Learning Building Datasets Healthcare 122

Cloud Academy

JANUARY 27, 2022

Data engineering is the process of designing and implementing solutions to collect, store, and analyze large amounts of data. This process is generally called “Extract, Transfer, Load” or ETL. The format of the data will be different depending on the intended audience.

Certification 52

Certification Data Engineering Data Engineer Engineering 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

Ascend.io

DECEMBER 14, 2022

How ETL Became Outdated The ETL process (extract, transform, and load) is a data consolidation technique in which data is extracted from one source, transformed, and then loaded into a target destination. In the 1980s, companies started to amass big amounts of transactional data. This causes two issues.

Data Integration 52

Data Integration Raw Data Data Consolidation Data Warehouse 52

Ascend.io

SEPTEMBER 14, 2023

The rise of data-intensive operations has positioned data engineering at the core of today’s organizations. As the demand to efficiently collect, process, and store data increases, data engineers have started to rely on Python to meet this escalating demand. Typing Dynamically typed, but can use type hints.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

Grouparoo

NOVEMBER 16, 2021

Breaking silos and collating data into a coherent set of information for processing will yield business benefits. This is where data transformation can come to the rescue. Traditional processes for data transformation follow the principle of Extract, Transform, and Load (ETL). To validate data and improve quality.

Data Mining 52

Data Mining Raw Data ETL Tools Unstructured Data 52

Knowledge Hut

SEPTEMBER 27, 2023

Parameters Microsoft Power BI Salesforce Basics purpose Developing reports, dashboard and business intelligence tool which takes data from sources and presents it as an information to the viewer. Customer relationship management (CRM) software which is used to manage, storing and generating leads for the user in the business process.

BI 52

BI Datasets Data Security Data Analysis 52

Meltano

OCTOBER 5, 2022

Data is becoming the world’s most valuable resource, according to an article in The Economist dating back to 2017. Since then, the way we compile, process, and store data has evolved significantly, and it continues to do so at incredible speed. The goal of DataOps is to speed up the process of deriving value from data.

Engineering 52

Engineering Raw Data Data Pipeline ETL Tools 52

The Modern Data Company

MARCH 1, 2024

For today’s Chief Data Officers (CDOs) and data teams, the struggle is real. We’re drowning in data yet thirsting for actionable insights. We need a new approach, a paradigm shift that delivers data with the agility and efficiency of a speedboat – enter Data Products.

Database-centric 40

Database-centric Data Lake Government Retail 40

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

Monte Carlo

FEBRUARY 15, 2023

Since the inception of the cloud, there has been a massive push to store any and all data. On the surface, the promise of scaling storage and processing is readily available for databases hosted on AWS RDS, GCP cloud SQL and Azure to handle these new workloads. Cloud data warehouses solve these problems.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Edureka

JANUARY 18, 2024

For those looking to start learning in 2024, here is a data science roadmap to follow. What is Data Science? Data science is the study of data to extract knowledge and insights from structured and unstructured data using scientific methods, processes, and algorithms.

Data Science 40

Data Science Deep Learning Machine Learning NoSQL 40

Edureka

JANUARY 23, 2023

Data mining is a method that has proven very successful in discovering hidden insights in the available information. It was not possible to use the earlier methods of data exploration. Through this article, we shall understand the process and the various data mining functionalities. What Is Data Mining?

Data Mining 52

Data Mining Banking Retail Medical 52

U-Next

OCTOBER 27, 2022

You might be wondering, “ what is data cleaning?” ” It is an integral part of a process where the data needs to be cleaned before it can be used effectively by any organization or business. In recent times, the importance of data has increased exponentially. What Is Data Cleaning? .

IT 40

IT Data Cleanse Datasets Bytes 40

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?

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

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. When necessary, train and retrain systems.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

Picnic Engineering

JULY 24, 2023

Introduction Transformer models have revolutionized natural language processing with their human-like text generation capabilities. By converting raw data into valuable information, transformer models could significantly contribute to sustainability. We enhance dataset diversity by applying random horizontal flips and rotations.

Datasets 52

Datasets Architecture Utilities Raw Data 52

Ascend.io

DECEMBER 11, 2023

What Is Data Orchestration? Data orchestration is the process of efficiently coordinating the movement and processing of data across multiple, disparate systems and services within a company. The goal is to minimize manual intervention and maximize the efficiency of data use.

Data Workflow 52

Data Workflow Data Pipeline Data Lake Data 52

AltexSoft

NOVEMBER 17, 2021

Additionally, we’ll explain how Natural Language Processing (NLP), Computer Vision, and Optical Character Recognition (OCR) are applied to document classification. Document classification is a process of assigning categories or classes to documents to make them easier to manage, search, filter, or analyze. Document scan classification.

Machine Learning 75

Machine Learning Insurance Medical Healthcare 75

AltexSoft

OCTOBER 22, 2021

Machine learning (ML) is a field of knowledge that relies on various algorithms to train computer systems on historical data so that they can make accurate predictions and decisions on their own when exposed to new data inputs. Neurons are individual nodes that are responsible for data flow and computations.

Deep Learning 52

Deep Learning Raw Data Algorithm Machine Learning 52

Knowledge Hut

JUNE 20, 2023

Moreover, the professionals can also use it in their work process. Theano It is an open-source Python library for deep learning in neural processing and data science. With this tool, the creation and distribution of the networks get sorted, and you can conveniently handle larger datasets.

Deep Learning 52

Deep Learning Data Science Python Raw Data 52

Monte Carlo

DECEMBER 19, 2022

Getting started with data quality testing? Here are the 7 must-have checks to improve data quality and ensure reliability for your most critical assets. Data quality testing is the process of validating that key characteristics of a dataset match what is anticipated prior to its consumption. million per year.

High Quality Data 52

High Quality Data Data SQL Bytes 52

ProjectPro

FEBRUARY 16, 2023

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. In addition to processing one or more DML operations on a database, stored procedures can accept user input and execute SQL commands.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 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.

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

Monte Carlo

JUNE 23, 2022

Volume has reigned supreme at the forefront of the data engineer’s psyche because, in the pre-Snowflake/AWS/Databricks era, the ability to store and process large volumes of data was seen as the primary architectural obstacle to business value.

Big Data 40

Big Data Management Machine Learning Data Warehouse 40