In this post, we focus on operationalizing advanced settings for OpenSearch Dashboards tenants with programmatic ways, in particular with the Dashboards Advanced Settings API. For a deeper insight into multi-tenancy in OpenSearch, refer to OpenSearch Dashboards multi-tenancy.
This post demonstrates how customers can use Amazon Kinesis Firehose Dynamic Partitioning to split the data by table, shape (by message schema/version), and by desired partitions on the fly to do this first step of data enrichment while ingesting data.
In this post, we share how SEEK Asia modernized their search-based system with a continuous integration and continuous delivery (CI/CD) pipeline and Amazon OpenSearch Service (successor to Amazon Elasticsearch Service).
The External Functions feature allows developers to invoke external APIs and remote services from within their Snowflake SQL queries and blend the response into their query result, in the same way as if they were internal Snowflake functions, eliminating the need to export and reimport data.
With EMR Serverless, you can run applications built using open-source frameworks such as Apache Spark, Hive, and Presto without having to configure, manage, optimize, or secure clusters.Tens of thousands of customers use Amazon EMR, a managed service for running open-source analytics frameworks such as Apache Spark, Hive, and Presto for large-scale data analytics applications.
We performed benchmark testing on our latest engine using TPC-DS benchmark queries at 3 TB scale and observed that query performance improved by 3x and cost decreased by 70% as a result of reduced scanned data size when compared to our earlier engine.
Dynamic defaults, however, also allow you to handle conditional rendering of visuals using parameter settings that allow you to personalize dashboards and email reports by the user by showing and hiding visuals as needed.
With AWS re:Invent just around the corner, we at the Amazon QuickSight team have put together this post to provide you with a handy list of all the key updates this year. We’ve broken this post into three key sections: insights for every user, embedded analytics with QuickSight, scaling and governance.
Amazon QuickSight is a fully-managed, cloud-native business intelligence (BI) service that makes it easy to connect to your data, create interactive dashboards, and share these with tens of thousands of users, either directly within QuickSight application, or embedded in web apps and portals.
At Singular, we have a pipeline that ingests data about ad views, ad clicks, and app installs from millions of mobile devices worldwide. This huge mass of data is aggregated on an hourly and daily basis. We enrich it with various marketing metrics and offer it to our customers to analyze their campaigns’ performance and see their ROI.Â
This post discusses a solution for running data reliability checks before loading the data into a target table in Amazon Redshift using the open-source library Great Expectations. You can automate the process for data checks via the extensive built-in Great Expectations glossary of rules using PySpark, and it’s flexible for adding or creating new customized rules for your use case.
CI/CD in the context of application development is a well-understood topic, and developers can choose from numerous patterns and tools to build their pipelines to handle the build, test, and deploy cycle when a new commit gets into version control.
Extract, transform, and load (ETL) orchestration is a common mechanism for building big data pipelines. Orchestration for parallel ETL processing requires the use of multiple tools to perform a variety of operations. To simplify the orchestration, you can use AWS Glue workflows.
Amazon QuickSight is a scalable business intelligence (BI) service built for the cloud, which allows insights to be shared with all users in the organization. QuickSight offers SPICE, an in-memory, cloud-native data store that allows end-users to interactively explore data.
