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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

Data Lake 103
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Create an Apache Hudi-based near-real-time transactional data lake using AWS DMS, Amazon Kinesis, AWS Glue streaming ETL, and data visualization using Amazon QuickSight

AWS Big Data

Data analytics on operational data at near-real time is becoming a common need. Due to the exponential growth of data volume, it has become common practice to replace read replicas with data lakes to have better scalability and performance. For more information, see Changing the default settings for your data lake.

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Enhance monitoring and debugging for AWS Glue jobs using new job observability metrics, Part 3: Visualization and trend analysis using Amazon QuickSight

AWS Big Data

Typically, you have multiple accounts to manage and run resources for your data pipeline. Publish the QuickSight dashboard When the analysis is ready, complete the following steps to publish the dashboard: Choose PUBLISH. Select Publish new dashboard as , and enter GlueObservabilityDashboard.

Metrics 106
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Access Amazon Athena in your applications using the WebSocket API

AWS Big Data

Many organizations are building data lakes to store and analyze large volumes of structured, semi-structured, and unstructured data. In addition, many teams are moving towards a data mesh architecture, which requires them to expose their data sets as easily consumable data products.

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Set up advanced rules to validate quality of multiple datasets with AWS Glue Data Quality

AWS Big Data

It supports both data quality at rest and data quality in AWS Glue extract, transform, and load (ETL) pipelines. Data quality at rest focuses on validating the data stored in data lakes, databases, or data warehouses. It ensures that the data meets specific quality standards before it is consumed.

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10 Things AWS Can Do for Your SaaS Company

Smart Data Collective

Data storage databases. Your SaaS company can store and protect any amount of data using Amazon Simple Storage Service (S3), which is ideal for data lakes, cloud-native applications, and mobile apps. Well, let’s find out. Artificial intelligence (AI). SNS supports push messaging to mobile devices. Easy to use.

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Automate deployment of an Amazon QuickSight analysis connecting to an Amazon Redshift data warehouse with an AWS CloudFormation template

AWS Big Data

In QuickSight, you analyze and visualize your data in analyses. When you’re finished, you can publish your analysis as a dashboard to share with others in your organization. Create an Amazon Redshift data source in AWS CloudFormation In this step, we add the AWS::QuickSight::DataSource section of the CloudFormation template.