eweekchat

A2. Ah - bit of a challenging question given some potential overlap among us.But I will give it a go with all due respect to my fellow panelists. :-)

A2. Scality's solution is available in both enterprise and open source editions. We can orchestrate data between legacy on-prem solutions and the public cloud. We support both file and object data formats. And we only require a single instance running on-prem or in cloud while...

...some solutions require running an instance in every location where data needs to be managed. We also support "in-band" and "out-of-band" operations - apps can write to Scality or to the storage location directly and we'll pick up the data after the write.

We also provide data fabric orchestration services (via Fabric Orchestrator) to our customers, spanning all major public clouds and any on premises environment. This allows organizations to have the right data, in the right place, at the right time.

[App + Data == Workload] While K8s and containers offer the common solution for App portability between clouds (public, private, hybrid), without comprehensive data orchestration capabilities, one can't achieve true Workload portability.

Our customers derive the benefit of enhanced performance against their data while leveraging all the data regardless of where it lies. This leads to better historical trending, outlier identification and multi-platform support.

Cool! So do you create a marshaling area of data, like a cache? Or if not, how exactly does the data get provisioned from where it lives, to where the user needs it for analysis or reporting? Can can you also enable OLTP applications with this method? #eWeekChat

Thanks and welcome Chris! Can you explain a bit more about the use of ML in this context?

@eric_kavanagh - mainly an OLAP platform. Our Acceleration Structures are not cache. They are not transient. Instead the are heuristically defined and maintained by AtScale.

AtScale is tracking queries and query patterns to ‘learn’ and ‘anticipate’ future behavior.

A1. At @BMCSoftware, we use #dataorchestration to bring our #data closer to compute across clusters, regions, clouds, and countries #eweekchat

Hi Sam, and welcome. What advantages are there in bringing compute closer to the data store itself?

A1. I can speak for a couple of our customers and open source users. We see people orchestrating data between legacy and modern applications - example, media company with a legacy FTP-based app orchestrating data up to cloud services.

A1. At @ascend_io, #dataorchestration is at the heart of our product, and what we also use internally to collect data across disparate systems & clouds, perform complex transformations & modeling (for #analytics and #datascience), and even automate #datagovernance.

Bringing the data from the datastore itself, closer to the compute or local to the compute has a number of advantages. Reduces network latency, increases throughput for the application itself and minimizes overhead with communicating to the datastore.

A1. At @alluxio , we focus on bringing #dataorchestration to our users, for hybrid cloud and multi cloud environments. Data Orchestration is accelerating compute with remote data and unify data access.

hi Chris, data orchestration in general simplifies data ecosystems due to new frameworks, cloud adoption/migration, as well as the rise of data-driven and demanded applications #eweekchat

A1. Business value of #dataorchestration can range from delivering new services to business continuity / DR.

A1. Users like DBS Bank and Walmart, have data on-prem. Accessing this data in other remote environments can be a big challenge and these users and others, use @alluxio 's "Zero-copy" bursting approach to bring data to the cloud to accelerate #bigdata compute

@dborkar I love @Alluxio's approach of creating a virtual cache of data that connects on-prem environments to each other, and to the cloud. As such, it enables high-powered analytics on data that's scattered across multiple locations. #DataOrchestration #eWeekChat