Silectis

FEBRUARY 17, 2021

In this blog post, we’ll walk you through how data science and data engineering are complementary disciplines. From the point of extraction from source systems, data must be structured and logically organized before it can flow to the target environment. We’ll also delineate a third category: data analysis.

Data Science 52

Data Science Data Engineering Data Engineer Engineering 52

dbt Developer Hub

NOVEMBER 28, 2021

Airflow and dbt are often framed as either / or: You either build SQL transformations using Airflow’s SQL database operators (like SnowflakeOperator ), or develop them in a dbt project. From the dbt side ​ That pipeline above included a plethora of data transformation jobs, built in various ways.

Google Cloud 52

Google Cloud SQL Cloud Consulting 52

phData: Data Engineering

MARCH 8, 2023

In this blog, we will explore what Fivetran is and how it works, as well as dive into its pricing structure to help you make an informed decision on whether or not Fivetran is the right platform for your data integration needs. What is Fivetran Used For? Why Use Fivetran? How Much Does Fivetran Cost?

IT 96

IT Data Warehouse Data Ingestion Data Integration 96

Ascend.io

APRIL 5, 2023

That usually means a) understanding the business use cases for data; and b) ensuring the right data is ingested, transformed, and available for the relevant analysis to support those use cases. For analytics engineers, understanding the business needs and transforming the data to meet them are two key steps.

Engineering 52

Engineering Raw Data BI Software Engineer 52

ProjectPro

JUNE 16, 2022

Amazon Web Services (AWS) offers an AWS Data Pipeline solution that helps businesses automate the transformation and movement of data. With AWS Data Pipeline, companies can quickly get started with Amazon Web Services and define data-driven workflows and parameters of data transformations.

Data Pipeline 59

Data Pipeline AWS Amazon Web Services Data Consolidation 59

ProjectPro

AUGUST 24, 2021

And, out of these professions, this blog will discuss the data engineering job role. So, work on projects that guide you on how to build end-to-end ETL/ELT data pipelines. In this use case, you will learn how to get streaming data from an API, cleanse data, transform it to get insights, and visualize the data in a dashboard.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

AltexSoft

JANUARY 26, 2022

Their role entails transforming, testing, and documenting data. Data engineers build data pipelines and perform ETL — extract data from sources, transform it, and load it into a centralized repository like a data warehouse. They can perform complex data transformations in SQL using dbt.

Engineering 59

Engineering Software Engineer Software Engineering Data Warehouse 59

AltexSoft

NOVEMBER 7, 2022

In complex pipelines, streaming platforms may ingest and process live data from a variety of sources, storing it in a repository while Airflow periodically triggers workflows that transform and load data in batches. The approach finds its application, for example, in ETL and ELT pipelines with many data sources or destinations.

PostgreSQL 52

PostgreSQL Metadata Python MySQL 52

U-Next

JUNE 29, 2022

In this blog, we’ll go over elements of big data , the big data environment as a whole, big data infrastructures, and some valuable tools for getting it all done. Transformation. Preparing data for analysis is known as extract, transform and load (ETL). Extract Load and transform (ELT) .

Big Data 40

Big Data Big Data Ecosystem Data Lake Raw Data 40

Snowflake

MARCH 30, 2023

Collecting, cleaning, and organizing data into a coherent form for business users to consume are all standard data modeling and data engineering tasks for loading a data warehouse. Data Vault explicitly defines a set of standards called “hard rules” to automate data extraction, cleaning, and modeling into raw vault tables.

Engineering 81

Engineering Raw Data Data Science Scala 81

Cloudera

JANUARY 11, 2021

Kafka Connect can answer some of the questions, but it is not a universal solution when you require complex filtering, routing, enrichment and transformations when moving data. However, one should consider the kind of processing/transformation required by the use case. All of these unique requests can scale very well with NiFi.

Kafka 59

Kafka Data Cleanse Java Datasets 59

phData: Data Engineering

MAY 11, 2023

In this blog post, we’ll discuss use cases for Zero-Code Development Life Cycle (ZDLC) in Matillion. In analytics applications, however, a body of batched data (or even streamed data) extracted from the transactional systems moves inexorably in one direction; from ingestion to data product. When Is ZDLC Better Than SDLC?

Coding 52

Coding Software Engineer Software Engineering Government 52

Databand.ai

JULY 28, 2022

In this very simplified example, we can see an ELT: Some pipeline tasks, probably running by Airflow , are scraping external data sources and collecting data from there. Those tasks are saving the extracted data in the data lake (or warehouse or lakehouse). The system can then read those tags to visualize the data lineage.

Metadata 52

Metadata Data Lake Datasets Data Warehouse 52

Monte Carlo

JULY 28, 2022

As GoCardless team lead and senior data engineer Andrew Jones recently wrote in his blog post about data contracts , “What I found in my swim upstream were well meaning engineers modifying services unaware that something as simple as dropping a field could have major implications on dashboards (or other consumers) downstream.”

Pipeline-centric 52

Pipeline-centric Software Engineer Software Engineering Database-centric 52

AltexSoft

MAY 14, 2021

Raw data needs to undergo the processes of extraction, transformation, and loading, so either ETL or ELT data pipelines are built to deliver data from sources to centralized repositories for further storage and processing. Data storage and processing. Apache Spark.

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

JANUARY 31, 2023

In this blog, we'll dive into some of the most commonly asked big data interview questions and provide concise and informative answers to help you ace your next big data job interview. Feature selection refers to extracting only the essential features from Big data. But the concern is - how do you become a big data professional?

Big Data 52

Big Data Hadoop AWS Relational Database 52

phData: Data Engineering

JULY 18, 2023

Generally, organizations need to integrate a wide variety of source systems when building their analytics platform, each with its own specific data extraction requirements. This complexity often necessitates the involvement of numerous experts who specialize in these individual systems to effectively extract the data.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52