API Life Cycle Basics: Clients
I broke this area of my research into a separate stop a couple years back, as I saw several new types of service providers emerging to provide a new type of web-based API client. These new tools allowed you to consume, collaborate, and put APIs to use without writing any code. I knew that this shift was going to be significant, even though it hasn’t played out as I expected, with most of the providers disappearing, or being acquired, and leaving just a handful of solutions that we see today.
These new web API clients allow for authentication, and the ability to quickly copy and paste API URLs, or the importing of API definitions to begin making requests and seeing responses for targeted APIs. These clients were born out of earlier API explorers and interactive API documentation, but have matured into standalone services that are doing interesting things with how we consume APIs. Here are the three web API clients I recommend you consider as part of your API lifecycle. https://goo.gl/2Npfkc #DataIntegration #ML
5 Data Integration Trends That Will Define the Future of ETL in 2018
ICYDK: ETL refers to extract, transform, load, and it is generally used for data warehousing and data integration. ETL is a product of the relational database era and it has not evolved much in the last decade. With the arrival of new cloud-native tools and platforms, ETL is becoming obsolete. There are several emerging data trends that will define the future of ETL in 2018. A common theme across all these trends is to remove the complexity by simplifying data management as a whole. In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures.
Unified Data Management Architecture
A unified data management (UDM) system combines the best of data warehouses, data lakes, and streaming without expensive and error-prone ETL. It offers reliability and performance of a data warehouse, real-time and low-latency characteristics of a streaming system, and scale and cost-efficiency of a data lake. More importantly, UDM utilizes a single storage backend with benefits of multiple storage systems which avoids moving data across systems hence avoiding data duplication and data consistency issues. Overall, it creates less complexity to deal with. https://goo.gl/gSdf6c #DataIntegration #ML