Tag: Donnie Prakoso

It is increasingly common to use multiple cloud services as building blocks to assemble a modern event-driven application. Using purpose-built services to accomplish a particular task ensures developers get the best capabilities for their use case. However, communication between services can be difficult if they use different technologies to communicate, meaning that you need to learn the nuances of each service and how to integrate them with each other. We usually need to create integration code (or “glue” code) to connect and bridge communication between services. Writing glue code slows our velocity, increases the risk of bugs, and means we spend our time writing undifferentiated code rather than building better experiences for our customers. Introducing Amazon EventBridge Pipes Today, I’m…

To build machine learning models, machine learning engineers need to develop a data transformation pipeline to prepare the data. The process of designing this pipeline is time-consuming and requires a cross-team collaboration between machine learning engineers, data engineers, and data scientists to implement the data preparation pipeline into a production environment. The main objective of Amazon SageMaker Data Wrangler is to make it easy to do data preparation and data processing workloads. With SageMaker Data Wrangler, customers can simplify the process of data preparation and all of the necessary steps of data preparation workflow on a single visual interface. SageMaker Data Wrangler reduces the time to rapidly prototype and deploy data processing workloads to production, so customers can easily integrate…

Data fuels machine learning. In machine learning, data preparation is the process of transforming raw data into a format that is suitable for further processing and analysis. The common process for data preparation starts with collecting data, then cleaning it, labeling it, and finally validating and visualizing it. Getting the data right with high quality can often be a complex and time-consuming process. This is why customers who build machine learning (ML) workloads on AWS appreciate the ability of Amazon SageMaker Data Wrangler. With SageMaker Data Wrangler, customers can simplify the process of data preparation and complete the required processes of the data preparation workflow on a single visual interface. Amazon SageMaker Data Wrangler helps to reduce the time it…

When Jeff Barr first announced Amazon Athena in 2016, it changed my perspective on interacting with data. With Amazon Athena, I can interact with my data in just a few steps—starting from creating a table in Athena, loading data using connectors, and querying using the ANSI SQL standard. Over time, various industries, such as financial services, healthcare, and retail, have needed to run more complex analyses for a variety of formats and sizes of data. To facilitate complex data analysis, organizations adopted Apache Spark. Apache Spark is a popular, open-source, distributed processing system designed to run fast analytics workloads for data of any size. However, building the infrastructure to run Apache Spark for interactive applications is not easy. Customers need…