Knowledge Hut

APRIL 19, 2023

It is the process of extracting data from various sources, transforming it into a format suitable for analysis, and loading it into a target database or data warehouse. ETL is used to integrate data from different sources and formats into a single target for analysis. What is an ETL Pipeline?

Data Warehouse 52

Data Warehouse Business Intelligence ETL Tools Data Pipeline 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

AltexSoft

AUGUST 4, 2021

Instead of combing through the vast amounts of all organizational data stored in a data warehouse, you can use a data mart — a repository that makes specific pieces of data available quickly to any given business unit. What is a data mart? Data mart vs data warehouse vs data lake vs OLAP cube.

Data Lake 64

Data Lake Data Warehouse ETL Tools Telecommunication 64

Data Engineering Podcast

JANUARY 15, 2022

Summary Applications of data have grown well beyond the venerable business intelligence dashboards that organizations have relied on for decades. Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud.

Engineering 100

Engineering Electronics ETL Tools Data Pipeline 100

ProjectPro

JANUARY 27, 2023

Over the past few years, data-driven enterprises have succeeded with the Extract Transform Load (ETL) process to promote seamless enterprise data exchange. This indicates the growing use of the ETL process and various ETL tools and techniques across multiple industries.

BI 52

BI ETL Tools Retail Healthcare 52

AltexSoft

OCTOBER 30, 2021

Such visualizations as graphs and charts are typically prepared by data analysts or business analysts, though not every project has those people employed. Then, a data scientist uses complex business intelligence tools to present business insights to executives. Managing data and metadata.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98

Monte Carlo

APRIL 4, 2023

With today’s announcement, Monte Carlo becomes the first data company to bring data observability to the entire orchestration layer, this coming after integrations with Airflow, dbt Core and dbt Cloud were announced in 2022.

ETL Tools 52

ETL Tools Data Pipeline Business Intelligence Data Warehouse 52

Monte Carlo

APRIL 4, 2023

With today’s announcement, Monte Carlo becomes the first data company to bring data observability to the entire orchestration layer, this coming after integrations with Airflow, dbt Core and dbt Cloud were announced in 2022.

ETL Tools 52

ETL Tools Data Pipeline Business Intelligence Data Warehouse 52

AltexSoft

JANUARY 26, 2022

If we take the more traditional approach to data-related jobs used by larger companies, there are different specialists doing narrowly-focused tasks on different sides of the project. 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.

Engineering 59

Engineering Software Engineer Software Engineering Data Warehouse 59

AltexSoft

OCTOBER 8, 2021

Data integration defines the process of collecting data from a number of disparate source systems and presenting it in a unified form within a centralized location like a data warehouse. So, why is data integration such a big deal? On-premise data integration tools. Cloud-based data integration tools.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

ProjectPro

FEBRUARY 18, 2022

Why is ETL used in Data Science? ETL stands for Extract, Transform, and Load. It entails gathering data from numerous sources, converting it, and then storing it in a new single data warehouse. ETL is a must-have for data-driven businesses. Use Airflow DAGs for processing the data.

Project 52

Project AWS Kafka Healthcare 52

AltexSoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. A publisher (say, telematics or Internet of Medical Things system) produces data units, also called events or messages , and directs them not to consumers but to a middleware platform — a broker.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93

ProjectPro

NOVEMBER 30, 2021

That's where the ETL (Extract, Transform, and Load) pipeline comes into the picture! Table of Contents What is ETL Pipeline? First, we will start with understanding the Data pipelines with a straightforward layman's example. Now let us try to understand ETL data pipelines in more detail.

Process 52

Process Data Pipeline Data Warehouse AWS 52

AltexSoft

SEPTEMBER 10, 2021

Data integration is the process of taking data from multiple disparate internal and external sources and putting it in a single location (e.g., data warehouse ) to achieve a unified view of collected data. So, why does anyone need to integrate data in the first place? Key types of data integration.

Data Integration 52

Data Integration Data Consolidation Data Warehouse BI 52

phData: Data Engineering

MAY 11, 2023

Practices centered on software engineering principles can create a barrier to entry for teams with skilled data wranglers looking to take their infrastructure to the next level with cloud-native tools like Matillion for the Snowflake Data Cloud. This is often called CURATED, or REPORT, or even DATA WAREHOUSE.

Coding 52

Coding Software Engineer Software Engineering Government 52

ProjectPro

SEPTEMBER 26, 2021

Many organizations are willing to pay 20-30% more to their Data Engineers than to Data Scientists. Google Trends shows the large-scale demand and popularity of Big Data Engineer compared with other similar roles, such as IoT Engineer, AI Programmer, and Cloud Computing Engineer. Who is a Big Data Engineer?

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

ProjectPro

JULY 27, 2021

Top 100+ Data Engineer Interview Questions and Answers The following sections consist of the top 100+ data engineer interview questions divided based on big data fundamentals, big data tools/technologies, and big data cloud computing platforms. Data is regularly updated. Takes up more space.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Monte Carlo

SEPTEMBER 8, 2021

Modern data teams have all the right solutions in place to ensure that data is ingested, stored, transformed, and loaded into their data warehouse, but what happens at “the last mile?” In other words, how can data analysts and engineers ensure that transformed, actionable data is actually available to access and use?

ETL Tools 52

ETL Tools Data Warehouse BI Data Pipeline 52

Cloudera

JUNE 16, 2022

CSP was recently recognized as a leader in the 2022 GigaOm Radar for Streaming Data Platforms report. Reduce ingest latency and complexity: Multiple point solutions were needed to move data from different data sources to downstream systems. Better yet, it works in any cloud environment. Getting started today.

Kafka 84

Kafka Manufacturing Data Lake SQL 84

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Scala 64

Scala Data Lake BI Google Cloud 64

Towards Data Science

NOVEMBER 4, 2023

I’d like to discuss some popular Data engineering questions: Modern data engineering (DE). Does your DE work well enough to fuel advanced data pipelines and Business intelligence (BI)? Are your data pipelines efficient? Often it is a data warehouse solution (DWH) in the central part of our infrastructure.

Data Engineering 86

Data Engineering Data Engineer Engineering BI 86