Precisely

SEPTEMBER 25, 2023

Data validation performs a check against existing values in a database to ensure that they fall within valid parameters. Data enrichment is the process of enhancing your data by appending relevant context from additional sources – improving its overall value, accuracy, and usability.

Data Validation 100

Data Validation Process Raw Data Data Cleanse 100

Christophe Blefari

APRIL 21, 2023

Data Engineering at Adyen — "Data engineers at Adyen are responsible for creating high-quality, scalable, reusable and insightful datasets out of large volumes of raw data" This is a good definition of one of the possible responsibilities of DE. Synthetic data are AI generated data.

Raw Data 130

Raw Data Data Datasets SQL 130

Precisely

NOVEMBER 20, 2023

We work with organizations around the globe that have diverse needs but can only achieve their objectives with expertly curated data sets containing thousands of different attributes. We assign a PreciselyID to every address in our database, linking each location to our portfolio’s vast array of data.

Raw Data 121

Raw Data Insurance Data Retail 121

Striim

NOVEMBER 17, 2023

Striim serves as a real-time data integration platform that seamlessly and continuously moves data from diverse data sources to destinations such as cloud databases, messaging systems, and data warehouses, making it a vital component in modern data architectures.

Machine Learning 52

Machine Learning Data Process PostgreSQL Process 52

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

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. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services.

Data Management 100

Data Management Management MongoDB Scala 100

Towards Data Science

AUGUST 31, 2023

Optimizing queries, improving runtimes, and geospatial data science applications Photo by Tamas Tuzes-Katai on Unsplash Intro: why is a spatial index useful? In doing geospatial data science work, it is very important to think about optimizing the code you are writing. This is where concepts such as spatial indices come in.

Data Engineering 69

Data Engineering Data Engineer Engineering Data Science 69

Monte Carlo

MAY 28, 2024

Ensuring all relevant data inputs are accounted for is crucial for a comprehensive ingestion process. Data Extraction : Begin extraction using methods such as API calls or SQL queries. Batch processing gathers large datasets at scheduled intervals, ideal for operations like end-of-day reports.

Data Ingestion 52

Data Ingestion Architecture Designing Hadoop 52

Christophe Blefari

JANUARY 14, 2023

import polars as pl Then I can do my first CSV import, in the example I load a French railway open dataset about lost and found objects in stations. df = pl.read_csv("lost-objects-stations.csv", sep=";") Then you can use the same code as pandas to select the data (head, ["col"], etc.). seed round.

Python 130

Python Kafka Data Scala 130

Ascend.io

NOVEMBER 21, 2023

More importantly, we will contextualize ELT in the current scenario, where data is perpetually in motion, and the boundaries of innovation are constantly being redrawn. Extract The initial stage of the ELT process is the extraction of data from various source systems. What Is ELT? So, what exactly is ELT?

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 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?

Machine Learning 52

Machine Learning Process Datasets Raw Data 52

Knowledge Hut

JULY 4, 2023

Data analysis and Interpretation: It helps in analyzing large and complex datasets by extracting meaningful patterns and structures. By identifying and understanding patterns within the data, valuable insights can be gained, leading to better decision-making, and understanding of underlying relationships.

Machine Learning 98

Machine Learning Medical Algorithm Deep Learning 98

Knowledge Hut

JANUARY 29, 2024

In today's data-driven world, where information reigns supreme, businesses rely on data to guide their decisions and strategies. However, the sheer volume and complexity of raw data from various sources can often resemble a chaotic jigsaw puzzle.

Raw Data 52

Raw Data Data Mining Data Preparation Structured Data 52

Data Engineering Podcast

DECEMBER 16, 2019

Summary Building clean datasets with reliable and reproducible ingestion pipelines is completely useless if it’s not possible to find them and understand their provenance. The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform.

Metadata 100

Metadata PostgreSQL Datasets Data Warehouse 100

Databand.ai

AUGUST 30, 2023

Data testing tools: Key capabilities you should know Helen Soloveichik August 30, 2023 Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing and maintaining data quality. There are several types of data testing tools.

Data Cleanse 52

Data Cleanse Data Pipeline Datasets Data Validation 52

Knowledge Hut

OCTOBER 4, 2023

While the numbers are impressive (and a little intimidating), what would we do with the raw data without context? The tool will sort and aggregate these raw data and transport them into actionable, intelligent insights. Some of the supported data sources are, 1. Files Excel (.xlsx)

BI 52

BI Systems Raw Data Business Intelligence 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

Workfall

SEPTEMBER 18, 2023

It simplifies the process of extracting, transforming, and loading (ETL) data by providing connectors for a wide range of data sources and destinations. Whether you need to integrate data from databases, APIs, cloud services, or other systems, Airbyte provides the tools to make it easier and more efficient.

Data Pipeline 93

Data Pipeline Raw Data Data Schemas Healthcare 93

Ascend.io

SEPTEMBER 14, 2023

High Performance Python is inherently efficient and robust, enabling data engineers to handle large datasets with ease: Speed & Reliability: At its core, Python is designed to handle large datasets swiftly , making it ideal for data-intensive tasks. Exceptional at data retrieval and manipulation within RDBMS.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

Knowledge Hut

DECEMBER 7, 2023

Given the rising importance of data with each passing day, I believe I will continue doing so in the coming years. Introducing Microsoft Power BI , a leading solution in this domain, which enables users to transform raw data into insightful visualizations and reports.

BI 52

BI Certification Business Intelligence Raw Data 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

ProjectPro

JANUARY 19, 2022

Source: Image uploaded by Tawfik Borgi on (researchgate.net) So, what is the first step towards leveraging data? The first step is to work on cleaning it and eliminating the unwanted information in the dataset so that data analysts and data scientists can use it for analysis.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Knowledge Hut

JANUARY 18, 2024

It entails using various technologies, including data mining, data transformation, and data cleansing, to examine and analyze that data. Both data science and software engineering rely largely on programming skills. However, data scientists are primarily concerned with working with massive datasets.

Software Engineer 97

Software Engineer Software Engineering Data Science Engineering 97

Knowledge Hut

JANUARY 24, 2024

This blog offers an exclusive glimpse into the daily rituals, challenges, and moments of triumph that punctuate the professional journey of a data scientist. The primary objective of a data scientist is to analyze complex datasets to uncover patterns, trends, and valuable information that can aid in informed decision-making.

Database-centric 52

Database-centric Data Science Machine Learning Datasets 52

Knowledge Hut

DECEMBER 22, 2023

Power BI is a technology-driven business intelligence tool or an array of software services, apps, and connectors to convert unrelated and raw data into visually immersive, coherent, actionable, and interactive insights and information. Microsoft developed it and combines business analytics, data visualization, and best practices.

BI 98

BI Business Intelligence Machine Learning SQL 98

Workfall

JULY 18, 2023

Behind the scenes, a team of data wizards tirelessly crunches mountains of data to make those recommendations sparkle. As one of those wizards, we’ve seen the challenges we face: the struggle to transform massive datasets into meaningful insights, all while keeping queries fast and our system scalable.

Datasets 77

Datasets Entertainment Data Workflow Data 77

AltexSoft

AUGUST 29, 2023

The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. This article explains what a data lake is, its architecture, and diverse use cases. Data sources can be broadly classified into three categories.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Monte Carlo

APRIL 24, 2023

By accommodating various data types, reducing preprocessing overhead, and offering scalability, data lakes have become an essential component of modern data platforms , particularly those serving streaming or machine learning use cases. With strong G2 scores (4.7

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Knowledge Hut

JANUARY 19, 2024

Embracing data science isn't just about understanding numbers; it's about wielding the power to make impactful decisions. Imagine having the ability to extract meaningful insights from diverse datasets, being the architect of informed strategies that drive business success. That's the promise of a career in data science.

Data Science 52

Data Science Machine Learning Datasets Algorithm 52

Edureka

AUGUST 2, 2023

Python offers a strong ecosystem for data scientists to carry out activities like data cleansing, exploration, visualization, and modeling thanks to modules like NumPy, Pandas, and Matplotlib. SQL Structured Query Language, or SQL, is used to manage and work with relational databases.

Programming Language 52

Programming Language Programming Scala Pharmaceutical 52

Knowledge Hut

DECEMBER 29, 2023

One of the primary focuses of a Data Engineer's work is on the Hadoop data lakes. NoSQL databases are often implemented as a component of data pipelines. Data engineers may choose from a variety of career paths, including those of Database Developer, Data Engineer, etc.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

Databand.ai

AUGUST 30, 2023

Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing, and maintaining data quality. There are several types of data testing tools. Data profiling tools: Profiling plays a crucial role in understanding your dataset’s structure and content.

Data Cleanse 52

Data Cleanse Data Validation Data Pipeline Datasets 52

AltexSoft

MAY 12, 2022

Another application of musical audio analysis is genre classification: Say, Spotify runs its proprietary algorithm to group tracks into categories (their database holds more than 5,000 genres ). For further steps, you need to load your dataset to Python or switch to a platform specifically focusing on analysis and/or machine learning.

Machine Learning 122

Machine Learning Building Datasets Healthcare 122

Knowledge Hut

OCTOBER 27, 2023

Since we've all been students, working with a dataset you're familiar with makes it easier to learn. Start by getting a dataset and splitting it into different performance parts. Practice Dataset Student Result 5. Weather data is abundant, and it offers unique variations and patterns.

BI 52

BI Project Datasets Healthcare 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. They develop self-running software.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

ProjectPro

DECEMBER 7, 2021

A pipeline may include filtering, normalizing, and data consolidation to provide desired data. It can also consist of simple or advanced processes like ETL (Extract, Transform and Load) or handle training datasets in machine learning applications.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Rockset

AUGUST 2, 2023

This enrichment data has changing schemas and new data providers are constantly being added to enhance the insights, making it challenging for Windward to support using relational databases with strict schemas. They used MongoDB as their metadata store to capture vessel and company data.

Database-centric 52

Database-centric PostgreSQL Transportation Insurance 52