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Unleashing the Power of Agentic AI: K2view Data Agent Builder Has Arrived

Unlock the power of no-code agentic AI to build intelligent, data-driven agents effortlessly. Leverage enterprise data for smarter, more efficient AI apps.

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Data Pipeline

April 5, 2022

What is Reverse ETL?

Although data warehouses and lakes are often associated with offline analytics, Reverse ETL can operationalize actionable insights to make a direct and ongoing...

Data Pipelining

January 27, 2022

iPaaS: A Better Approach to the Cloud Data Pipeline

With the rising need for self-service data integration, enterprises require a more effective cloud data pipeline solution. iPaaS, based on data products, is...

Data Pipelining

October 24, 2021

Building a Data Pipeline Architecture for the Enterprise

Building an effective data pipeline architecture is challenging, especially when enterprise requirements – like scale, speed, and security – are taken into...

Data Pipelining

October 7, 2021

What are ETL Tools? Advantages, Disadvantages and Innovations Revealed

Find out how the ETL method of data integration enhances enterprise data pipelines, and how eETL overcomes the most difficult challenges. Learn more here....

Data Pipelining

August 19, 2021

Avoid Data Lake Failures with Data Quality Management Best Practices

The growing dependence on big data is often met with statistics showing that many data lake projects fail because they did not adhere to data quality...

Data Pipelining

August 5, 2021

Data Integration with Data Products Optimizes your Data Pipeline

More than 80% of enterprises consider data integration a critical component in their ongoing business operations, so choosing the right way to integrate data...

Data Pipelining

July 27, 2021

ETL vs ELT vs eETL

What’s the difference between ETL vs ELT – and how does the new approach, “entity-based ETL” (eETL), effectively address their shortcomings?

Data Pipelining

July 20, 2021

Automated Data Preparation – 4 Issues and 4 Answers

Today’s “automated data preparation” tools aren’t really automated. This article discusses 4 key challenges on the road to automation, and 4 ways to overcome...

Data Pipelining

June 23, 2021

7 Data Preparation Steps to Cleaner Data Lakes

The data preparation process is critical, due to the importance of maintaining clean, high-quality data for operational and analytical workloads. Here are 7...

Data Pipelining

June 20, 2021

Your Data Preparation Process Needs Data Fabric, Not Standalone Tools

For your data preparation process, use a data fabric architecture on a Data Product Platform, instead of an assortment of standalone tools.

Data Fabric

June 20, 2021

Prepare Yourself: What is Data Preparation?

Theoretically, data preparation is the process of exploring, combining, cleaning, and transforming raw data into curated datasets for data integration, data...

Data Pipelining

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K2VIEW BLOG

Unleashing the Power of Agentic AI: K2view Data Agent Builder Has Arrived

data pipelining

Explore more content

What is Reverse ETL?

iPaaS: A Better Approach to the Cloud Data Pipeline

Building a Data Pipeline Architecture for the Enterprise

What are ETL Tools? Advantages, Disadvantages and Innovations Revealed

Avoid Data Lake Failures with Data Quality Management Best Practices

Data Integration with Data Products Optimizes your Data Pipeline

ETL vs ELT vs eETL

Automated Data Preparation – 4 Issues and 4 Answers

7 Data Preparation Steps to Cleaner Data Lakes

Your Data Preparation Process Needs Data Fabric, Not Standalone Tools

Prepare Yourself: What is Data Preparation?