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Data-Integration
Data integration is the process of combining data from various sources into a unified view, enabling organizations to analyze and utilize information effectively. This critical IT function supports analytics, machine learning, and artificial intelligence initiatives by ensuring high-quality, standardized data delivery. Data integration encompasses various technologies and methodologies, including ETL (Extract, Transform, Load), ELT (Extract, Load, Transform), and iPaaS (Integration Platform as a Service). By streamlining data flows between internal and external databases, data integration enhances decision-making and operational efficiency, making it an essential component of modern data management strategies.
Amazing cost savings with AWS Lambda for Data Integration — (2020)
Data Integration is a critical IT function that focuses on delivering high-quality standardized data to the enterprise, and thus enables Analytics, Machine Learning and AI initiatives to add business…...
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Data Integration: The Definitive Guide
This guide on data integration provides a thorough overview of the data integration landscape, describing the various technologies that are available to move data between tools and databases.
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Data Integration — Things to Consider
When integrating data from system A to system B, data engineers and other stakeholders should not only focus on the data process, e.g. via ETL/ELT, but also on the source system. What various…
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All Data Integrations Should Use Change Data Capture
Data integrations have been around for decades, but there has been a recent explosion of new, compelling data integration companies offering cloud-native, easy-to-configure connectors and quick…
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How We Can Commoditize Data Integration Pipelines
Most engineers in their professional life will have to deal with data integrations. In the past few years, a few companies such as Fivetran and StitchData have emerged for batch-based integrations…
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Data Integration Strategies for Time Series Databases
As digital transformation reaches more industries, the number of data points generated is growing exponentially. As such, data integration strategies to collect such large volumes of data from…
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Integrate API Data to a Database
using DLT, Postgres and Python Integrate API Data In today’s blog post, we’re exploring how to use an Application Programming Interface (API) as a data source. In our data-driven world, integrating da...
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Implementing the functional data engineering paradigm in data load processes by using Airflow
We integrate several data sources in a project comprising more than forty applications. Such sources are integrated by performing batch processes allowing for incrementally loading data from…
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5 Cloud Data Integration Softwares You Should Try
One of the biggest challenges in information systems is to manage the integration of data to provide access to different sectors in the business. Companies that drive their business towards data must…...
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Data Engineering: Incremental Data Loading Strategies
Years of serving as a data engineer and analyst working on integrating many data sources into enterprise data platforms, I managed to encounter one complexity after another when trying to incrementall...
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Introducing DagsHub: Complete Data Science Project Integration Platform
Code, Experiments, Model, and Data integration under one roof Continue reading on Towards Data Science
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Data Integration with Pandas
For adhoc data analysis via Jupyter Notebook you often need external data via CSV, Excel or database to process them with Python. Reading the data is relatively easy thanks to the handy Pandas…
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