Elastic Clusters enables a user to scale a document database to handle virtually any number of writes and reads, with petabytes of storage capacity.
Elastic Clusters simplifies the way customers interact with Amazon DocumentDB by automatically managing the underlying infrastructure and eliminating the need to create, remove, upgrade or scale instances.

Several parameters for Elastic Clusters

Sharding – A popular database concept also known as partitioning, partitioning splits large datasets into smaller datasets across multiple nodes, allowing customers to scale their database beyond the limits of vertical scaling. Elastic Clusters uses sharding to partition data in Amazon DocumentDB’s distributed storage system.

Scaling workloads with little or no impact – With Elastic Clusters, your database can scale to millions of operations with little or no interruption or performance impact.

Integration with other AWS services – Elastic Clusters integrates with other AWS services the same way Amazon DocumentDB integrates today. First, you can monitor the health and performance of your Elastic Clusters using Amazon CloudWatch. Second, you can set up authentication and authorization for resources such as clusters through AWS Identity and Access Management (IAM) users and roles, and use Amazon Virtual Private Cloud (Amazon VPC) for secure connections to VPC only. Finally, you can use AWS Glue to import and export data to and from other AWS services, such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, and Amazon OpenSearch Service.

Create a Cluster
When you create a cluster, you will specify the vCPUs you want for your Elastic Clusters when provisioning. With the amount of vCPUs you provision, you will also get a proportional amount of memory expressed in vCPUs. Elastic Clusters automatically provision the required infrastructure (parts and instances) on your behalf.

Customers pay for the amount of compute measured in vCPUs, for the database storage, and for the backup storage. DocumentDB Elastic Clusters is available in a subset of AWS regions, including Ohio, Northern Virginia, Frankfurt, and Ireland.

Previous versions of Lottie rendered animations in the application’s main thread, effectively using CADisplayLink. Once every frame, Lottie executes code on the main thread to accelerate the animation’s progress and re-render its content.

This system framework renders the animations out-of-process using GPU hardware acceleration. The playback of the animations is managed by a separate system process called a “rendering server”.

This new engine eliminates CPU load when playing back a Lottie animation and effectively ensures that Lottie animations will play back smoothly at 60 or 120 fps regardless of the application’s CPU load.

As of today’s Lottie 4.0 release for iOS, the Core Animation rendering engine is enabled by default for all apps using Lottie, with no additional work or migration required on the part of app developers.

Amazon Omics makes it easy to store, search and analyze data and then generate conclusions from it. It simplifies and speeds up the process of storing and analyzing multiomic information for research and clinical applications so that focusing on extracting insights from the data can come first.

General
Purpose-built storage
Amazon Omics storage is compatible with bioinformatics file formats such as FASTQ, BAM, and CRAM and allows you to store, discover, and share this data efficiently and at low cost. These file formats are stored as read-set objects within a sequence store. You can also store reference genomes in the FASTA format. Data is imported as immutable objects with unique identifiers to support workloads that require strict data provenance. Access to individual data objects, including references and read-set objects, can be controlled using tags and attribute-based access controls through AWS Identity and Access Management (IAM).

Bioinformatics workflows
Amazon Omics helps you run bioinformatics workflows at scale. Specify your workflow definition, the tools you want to use, and the data to analyze, and Amazon Omics will provision the underlying infrastructure and implement the workflow. Workflow definitions compliant with WDL 1.1 and Nextflow 22.10.0 DSL2 specifications are supported. Workflows use OCI-compliant containerized tooling stored in private registries in Amazon Elastic Container Registry (ECR).

Analysis at scale
With Amazon Omics, you can quickly ingest and transform genomics data formats such as (g)VCF, GFF3, and TSV/CSVs into Apache Parquet. You can make the genomics data accessible through analytics services such as Amazon Athena. You can transform both variant data (data from an individual sample) and annotation data (known information about positions in the genome). You can control access to analytics stores with AWS Lake Formation, making it easier to perform queries across diverse data sources while implementing fine-grained access controls.

Data collaboration and provenance
Amazon Omics makes it easier for researchers to tag collaborators, set up their permissions, and share data securely with them. This simplifies how you make your omics data findable, accessible, interoperable, and reusable (FAIR). With domain-specific metadata, you can link Amazon Omics data stores with other omics and healthcare data to facilitate multiomic and multimodal analysis.

Security, privacy, and compliance
HIPAA eligible
Amazon Omics is HIPAA eligible. You can apply attribute-based controls to define fine-grained data access and governance. Comprehensive logging and provenance capture is built in so you know what data was accessed, who accessed it, and when.