Knowledge Hut

MAY 2, 2024

A lot of organizations are moving to Spark as their ETL processing layer from legacy ETL systems like Informatica. Spark is a very good and optimized SQL processing module that fits the ETL requirements as it can read from multiple sources and can also write to many kinds of data sources.

Hadoop 96

Hadoop Scala Datasets Java 96

Striim

AUGUST 31, 2023

In conclusion, kappa architectures have revolutionized the way businesses approach big data solutions – allowing them to take advantage of cutting edge technologies while reducing costs associated with manual processes like ETL systems.

Data Integration 69

Data Integration Architecture Amazon Web Services Machine Learning 69

Precisely

APRIL 3, 2023

The Long Road from Batch to Real-Time Traditional “extract, transform, load” (ETL) systems were built under certain constraints, stemming from the cost of technology and implementation resources, as well as the inherent limits of computational power. Today’s world calls for a streaming-first approach.

ETL System 52

ETL System Transportation Architecture Manufacturing 52

Confluent

SEPTEMBER 12, 2019

In a use case like online ticketing, it may seem obvious that the transactional side of the system is well suited to an event processing architecture, but certain of the analytical requirements demand the same architecture.

Kafka 10

Kafka ETL System Architecture Retail 10

Knowledge Hut

NOVEMBER 28, 2023

An effective ETL system should also be designed to ingest data from potentially many different sources. The data storage platform you choose should be optimized to work effectively within your organization's budget constraints. After designing and setting up your database or data warehouse, you need to populate it with data.

Data Engineer 52

Data Engineer Data Engineering Engineering Generalist 52

ProjectPro

NOVEMBER 30, 2021

Incremental Extraction Each time a data extraction process runs (such as an ETL pipeline), only new data and data that has changed from the last time are collected—for example, collecting data through an API. Transform All the data science professionals would be familiar with the term "Garbage in, garbage out."

Process 52

Process Data Pipeline Data Warehouse AWS 52

Monte Carlo

APRIL 12, 2023

Oftentimes these ETL systems come under considerable pressure as all of your stakeholders want to look at every metric a million different ways with sub second latency. It’s hard to convince departments to launch experiments or executives to trust them if no one believes in the underlying data or the dashboards they look at every day.

Data 52

Data Programming ETL System Media 52

Monte Carlo

MAY 17, 2023

Oftentimes these ETL systems come under considerable pressure as all of your stakeholders want to look at every metric a million different ways with sub second latency. It’s hard to convince departments to launch experiments or executives to trust them if no one believes in the underlying data or the dashboards they look at every day.

Data 52

Data Data Pipeline Data Engineer Data Engineering 52

Monte Carlo

MAY 17, 2023

Oftentimes these ETL systems come under considerable pressure as all of your stakeholders want to look at every metric a million different ways with sub second latency. It’s hard to convince departments to launch experiments or executives to trust them if no one believes in the underlying data or the dashboards they look at every day.

Data Pipeline 52

Data Pipeline Data Engineer Data Engineering Data 52