Tag: Donnie Prakoso

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…

There are various ways to report on data insights, and paginated reports is one of them. Paginated reports are essential documents that contain critical business information for end-users. For decades, paginated reports have been the standard business reporting format. The following are examples of paginated reports. On the left shows the report for income statement and on the right is the yearly summary corporate statement: As the example shows, paginated reports contain various highly formatted insights and are designed to be printable, in landscape or portrait orientation, so they can be consumed easily by readers. It’s called paginated because it often spans tens of hundreds of pages of data. Although it may appear to be a simple task, generating paginated…