Category: AWS Step Functions

In this post, we discuss how AWS can help you successfully address the challenges of extracting insights from unstructured data. We discuss various design patterns and architectures for extracting and cataloging valuable insights from unstructured data using AWS. Additionally, we show how to use AWS AI/ML services for analyzing unstructured data.

In this post, we describe how Cognizant’s Data & Intelligence Toolkit (CDIT)- ETL Conversion Tool can help you automatically convert legacy ETL code to AWS Glue quickly and effectively. We also describe the main steps involved, the supported features, and their benefits.

AWS has invested in native service integration with Apache Hudi and published technical contents to enable you to use Apache Hudi with AWS Glue (for example, refer to Introducing native support for Apache Hudi, Delta Lake, and Apache Iceberg on AWS Glue for Apache Spark, Part 1: Getting Started). In AWS ProServe-led customer engagements, the use cases we work on usually come with technical complexity and scalability requirements. In this post, we discuss a common use case in relation to operational data processing and the solution we built using Apache Hudi and AWS Glue.

In this post we discuss how we can build and orchestrate in a few steps an ETL process for Amazon Redshift using Amazon S3 Event Notifications for automatic verification of source data upon arrival and notification in specific cases. And we show how to use AWS Step Functions for the orchestration of the data pipeline. It can be considered as a starting point for teams within organizations willing to create and build an event driven data pipeline from data source to data warehouse that will help in tracking each phase and in responding to failures quickly. Alternatively, you can also use Amazon Redshift auto-copy from Amazon S3 to simplify data loading from Amazon S3 into Amazon Redshift.

In the world of software engineering and development, organizations use project management tools like Atlassian Jira Cloud. Managing projects with Jira leads to rich datasets, which can provide historical and predictive insights about project and development efforts. Although Jira Cloud provides reporting capability, loading this data into a data lake will facilitate enrichment with other […]

Extracting time series on given geographical coordinates from satellite or Numerical Weather Prediction data can be challenging because of the volume of data and of its multidimensional nature (time, latitude, longitude, height, multiple parameters). This type of processing can be found in weather and climate research, but also in applications like photovoltaic and wind power. […]

Implementing an effective data sharing strategy that satisfies compliance and regulatory requirements is complex. Customers often need to share data between disparate software as a service (SaaS) platforms within their organization or across organizations. On many occasions, they need to apply business logic to the data received from the source SaaS platform before pushing it […]

An event-driven architecture is a software design pattern in which decoupled applications can asynchronously publish and subscribe to events via an event broker. By promoting loose coupling between components of a system, an event-driven architecture leads to greater agility and can enable components in the system to scale independently and fail without impacting other services. […]

This is a guest post co-authored by Mahesh Vandi Chalil, Chief Technology Officer of BookMyShow. BookMyShow (BMS), a leading entertainment company in India, provides an online ticketing platform for movies, plays, concerts, and sporting events. Selling up to 200 million tickets on an annual run rate basis (pre-COVID) to customers in India, Sri Lanka, Singapore, […]

Extract, Load, and Transform (ELT) is a modern design strategy where raw data is first loaded into the data warehouse and then transformed with familiar Structured Query Language (SQL) semantics leveraging the power of massively parallel processing (MPP) architecture of the data warehouse. When you use an ELT pattern, you can also use your existing […]