Ascend.io

AUGUST 31, 2023

In this article, we assess: The role of the data warehouse on one hand, and the data lake on the other; The features of ETL and ELT in these two architectures; The evolution to EtLT; The emerging role of data pipelines. However , to reduce the impact on the business, a data warehouse remains in use.

Data Lake 52

Data Lake Data Warehouse ETL Tools Data Pipeline 52

Data Engineering Weekly

DECEMBER 18, 2023

.” [link] Netflix: Our First Netflix Data Engineering Summit Netflix publishes the tech talk videos of their internal data summit. It is great to see an internal tech talk with a series focus on data engineering. My highlight is the talk about the data processing pattern around incremental data pipelines.

Data Engineering 68

Data Engineering Data Engineer Engineering Food 68

Monte Carlo

JULY 22, 2024

While it shares similarities with software versioning, data versioning has unique characteristics specific to your data management needs. Maintaining metadata about each version. By implementing data versioning, you can create a systematic approach to managing the evolution of your data.

Metadata 52

Metadata Datasets ETL Tools Data 52

Silectis

DECEMBER 15, 2020

Additionally, Magpie reduces your team’s IT complexity by eliminating the need to use separate data catalog, data exploration, and ETL tools. The whole data engineering process takes place directly within the platform, and eliminates the need to switch between different systems and tools. Or your team?

Data Engineering 52

Data Engineering Data Engineer Engineering ETL Tools 52

ProjectPro

JANUARY 24, 2023

A survey by Data Warehousing Institute TDWI found that AWS Glue and Azure Data Factory are the most popular cloud ETL tools with 69% and 67% of the survey respondents mentioning that they have been using them. AWS Glue provides the functionality required by enterprises to build ETL pipelines.

AWS 52

AWS Cloud Amazon Web Services ETL Tools 52

ProjectPro

JANUARY 24, 2023

With over 20 pre-built connectors and 40 pre-built transformers, AWS Glue is an extract, transform, and load (ETL) service that is fully managed and allows users to easily process and import their data for analytics. You can leverage AWS Glue to discover, transform, and prepare your data for analytics.

AWS 52

AWS Data Lake ETL Tools Scala 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. However, there is a range of open-source client libraries enabling you to build Kafka data pipelines with practically any popular programming language or framework. ZooKeeper issue.

Kafka 93

Kafka Hadoop Big Data ETL Tools 93

Grouparoo

FEBRUARY 9, 2022

Today, organizations are adopting modern ETL tools and approaches to gain as many insights as possible from their data. However, to ensure the accuracy and reliability of such insights, effective ETL testing needs to be performed. So what is an ETL tester’s responsibility? Metadata testing.

Process 52

Process ETL System Data Warehouse Metadata 52

Edureka

JULY 3, 2024

ADF connects to various data sources, including on-premises systems, cloud services, and SaaS applications. It then gathers and relocates information to a centralized hub in the cloud using the Copy Activity within data pipelines. Transform and Enhance the Data: Once centralized, data undergoes transformation and enrichment.

Pipeline-centric 52

Pipeline-centric Data Lake Database-centric Data Pipeline 52

Monte Carlo

OCTOBER 5, 2023

effective communication that’s essential for coordinating ETL tasks, managing dependencies, and ensuring that everyone is aware of schedules, downtimes, and changes. increased vigilance in maintaining thorough documentation and metadata. Your data pipelines will thank you.

Data Cleanse 52

Data Cleanse ETL Tools Datasets High Quality Data 52

Monte Carlo

SEPTEMBER 12, 2022

But with the rise of tools such as Segment, Fivetran, Meltano, and Airbyte, it’s become relatively easy for teams to bring all of their data from external sources into a centralized place like a data warehouse.

BI 52

BI Data Governance ETL Tools Data Warehouse 52

dbt Developer Hub

NOVEMBER 3, 2022

We use Snowflake as our data warehouse where we build dashboards both for internal use and for customers. In the past we relied upon an ETL tool (Stitch) to pull data out of microservice databases and into Snowflake. This data would become the main dbt sources used by our report models in BI. Let's talk!

Kafka 52

Kafka ETL Tools BI Database 52

Knowledge Hut

MAY 9, 2024

Responsibilities Responsibilities of data modelers include validating data models, evaluating existing systems, ensuring data consistency, and optimizing metadata. Skills Required Data modelers must be proficient in SQL, metadata management, data modeling, interpersonal communication, and statistical analysis.

Data Science 52

Data Science Data Architect Data Mining Programming Language 52

ProjectPro

SEPTEMBER 26, 2021

Becoming a Big Data Engineer - The Next Steps Big Data Engineer - The Market Demand An organization’s data science capabilities require data warehousing and mining, modeling, data infrastructure, and metadata management. Most of these are performed by Data Engineers.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

AltexSoft

AUGUST 29, 2023

Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture. This structure is made efficient by data engineering practices that include object storage. Watch our video explaining how data engineering works.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

dbt Developer Hub

NOVEMBER 28, 2021

In my days as a data consultant and now as a member of the dbt Labs Solutions Architecture team, I’ve frequently seen Airflow, dbt Core & dbt Cloud ( via the official provider ) blended as needed, based on the needs of a specific data pipeline, or a team’s structure and skillset.

Google Cloud 52

Google Cloud SQL Cloud Consulting 52

Monte Carlo

FEBRUARY 19, 2024

Self-service functionality — a data mesh allows users to abstract technical complexity and focus on self-serving their individual data use cases with a central platform that includes the data pipeline engines, storage, and streaming infrastructure.

Architecture 59

Architecture Government Data Data Architecture 59

Knowledge Hut

NOVEMBER 28, 2023

Let us take a look at the top technical skills that are required by a data engineer first: A. Technical Data Engineer Skills 1.Python Python is ubiquitous, which you can use in the backends, streamline data processing, learn how to build effective data architectures, and maintain large data systems.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

AltexSoft

MARCH 30, 2023

Source: The Data Team’s Guide to the Databricks Lakehouse Platform Integrating with Apache Spark and other analytics engines, Delta Lake supports both batch and stream data processing. Besides that, it’s fully compatible with various data ingestion and ETL tools.

Scala 64

Scala Data Lake BI Machine Learning 64

AltexSoft

OCTOBER 8, 2021

The prevailing part of users claim that it is quite easy to configure and manage data flows with Oracle’s graphical tools. Data profiling and cleansing. The toolkit allows you to quickly build data pipelines , automate integration tasks, and monitor jobs. Data loading. Source: Softwareadvice. Ease of use.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

Data Engineering Weekly

JANUARY 6, 2023

Author: Chris Ricommni Recap Github: [link] [Don't forget to star it] I’m excited to release Recap , a dead simple data catalog for engineers written in Python. Recap makes it easy for engineers to build infrastructure and tools that need metadata. Unlike traditional data catalogs, Recap is designed to power software.

Metadata 40

Metadata ETL Tools MySQL Data Lake 40

phData: Data Engineering

SEPTEMBER 27, 2024

Both persistent staging and data lakes involve storing large amounts of raw data. But persistent staging is typically more structured and integrated into your overall customer data pipeline. It’s not just a dumping ground for data, but a crucial step in your customer data processing workflow.

Data 52

Data Raw Data Data Lake Architecture 52

ProjectPro

NOVEMBER 30, 2021

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. The crux of all data-driven solutions or business decision-making lies in how well the respective businesses collect, transform, and store data.

Process 52

Process Data Pipeline Data Warehouse AWS 52