Protect Your Sensitive Data Throughout Its Cloud Analytics Journey

In the previous blog, Making enterprise resilient while accelerating secure cloud migration, we touched upon the need to persistently protect data upon ingestion, at rest, and while in use. We also discussed why continuous data protection mitigates data privacy challenges and other data security threats. This blog will highlight how our industry-leading enterprise data protection allows enterprises to pick and choose the technology that best fits their data security needs from data encryption, tokenization, pseudonymization and anonymization techniques.

Protect your sensitive data throughout its cloud analytics journeyVoltage SecureData enables a persistent data protection model in multi-cloud environments by removing the need for in-cloud decryption and has a broad range of certified integrations. As an enterprise, you can pick and choose any technology from the list below depending on what you might have in your environment. Whether you have deployed cloud data warehouses, analytics services offered by cloud services or other database related serverless service functions, Voltage SecureData has you covered:

  • Voltage SecureData integrations for cloud data warehouses, such as Snowflake, Amazon Redshift, Google BigQuery, and Azure Synapse, enables Voltage customers to conduct high-scale secure analytics and data science in the cloud using format-preserved, tokenized data that mitigates the risk of compromising business-sensitive information while adhering to privacy regulations.
  • Cloud ETL services, such as AWS Glue, Azure Data Factory, and Google Data Fusion, as well as other COTS ETL tools such as Informatica, Talend, DataStage, Ab Initio, and others.
  • Streaming platforms, such as Kafka, NiFi, Storm, Streamsets, and Cloud streaming services such as AWS Kinesis, Azure EventHubs, Google Dataflow, and others.
  • Data lake services, such as AWS Simple Storage Service (S3), Azure Blob storage, Google Cloud Storage, AWS RedShift, Azure Databricks, Azure SQL Data Warehouse/Synapse Analytics, Google BigQuery, AWS EMR, Azure HDInsight, Google Dataproc, Snowflake, and others.
  • SQL and NoSQL database services include AWS RDS, Aurora, and DynamoDB, Azure SQL Database, Cosmos DB, Google Cloud SQL, and others.
  • Additional capabilities include Voltage transformation on serverless compute services or Functions as a Service (FaaS), such as AWS Lambda, Azure Functions, and Google Cloud Functions, AWS Macie, AWS API Gateway, Google Data Catalog, Google Apigee, Azure Data Catalog. 

To conclude this blog series, companies looking to take advantage of cloud analytics need the ability to protect sensitive data persistently. A data-centric approach to protection allows you to meet privacy and security regulation requirements in a cost-effective way, maintain data usability and remain in control of your data security even when it has left your enterprise perimeter.  Visit our cloud analytics hub for more details on how Voltage can support your cloud analytics journey. 

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