K2view has been recognized by CIOCoverage magazine as 1 of the top 10 data integration companies to watch in 2023. Read on to learn why.
data pipelining
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...
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...
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...
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....
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 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...
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?
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...
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...
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.
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...
