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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

Effective DQM is recognized as essential to any consistent data analysis, as the quality of data is crucial to derive actionable and – more importantly – accurate insights from your information. There are a lot of strategies that you can use to improve the quality of your information. 2 – Data profiling.

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Gain insights from historical location data using Amazon Location Service and AWS analytics services

AWS Big Data

You can also use the data transformation feature of Data Firehose to invoke a Lambda function to perform data transformation in batches. Athena is used to run geospatial queries on the location data stored in the S3 buckets. Use DeviceId as an additional prefix to write the objects to the bucket. Choose Run.

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Cross-account integration between SaaS platforms using Amazon AppFlow

AWS Big Data

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. Let’s take an example.

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The Modern Data Stack Explained: What The Future Holds

Alation

These help data analysts visualize key insights that can help you make better data-backed decisions. ELT Data Transformation Tools: ELT data transformation tools are used to extract, load, and transform your data. Examples of data transformation tools include dbt and dataform.

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Exploring the AI and data capabilities of watsonx

IBM Big Data Hub

foundation models to help users discover, augment, and enrich data with natural language. Watsonx.data is built on 3 core integrated components: multiple query engines, a catalog that keeps track of metadata, and storage and relational data sources which the query engines directly access.

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

The system ingests data from various sources such as cloud resources, cloud activity logs, and API access logs, and processes billions of messages, resulting in terabytes of data daily. This data is sent to Apache Kafka, which is hosted on Amazon Managed Streaming for Apache Kafka (Amazon MSK).

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Build incremental data pipelines to load transactional data changes using AWS DMS, Delta 2.0, and Amazon EMR Serverless

AWS Big Data

The Delta tables created by the EMR Serverless application are exposed through the AWS Glue Data Catalog and can be queried through Amazon Athena. Data ingestion – Steps 1 and 2 use AWS DMS, which connects to the source database and moves full and incremental data (CDC) to Amazon S3 in Parquet format.