Google Cloud Dataflow
Amazon EMR
Google BigQuery
Qubole
Snowflake
Databricks
Apache Beam
Amazon Kinesis
Savee
Raindrop.io
AWS Snowball
Martechbase
Ethereum
My Mind
Zluri
Cubiloon
In today's business landscape, it's more important than ever for companies to scale rapidly and efficiently. However, this can be difficult when teams are siloed, and goals are disconnected. This leads to bloated technology footprints and unnecessary spending.
Savee is a VendorOS that helps businesses overcome these issues. It identifies vendor overlaps and potential compliance issues while uncovering cost savings and managing the approval and renewal processes. This helps savvy business leaders scale rapidly and efficiently.
To get started with Savee, simply visit the website and create an account. From there, you can browse the list of vendors and see how they can help your business save money.
Benefits of using Savee include: - Reduced spending on unnecessary technology products - Faster identification of vendor overlap and cost savings - Easier management of technology Vendor Relationships - Easier renewal management - Better visibility into company-wide spending on technology products
Google Cloud Dataflow
SaveeBased on our record, Google Cloud Dataflow should be more popular than Savee. It has been mentiond 14 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Imo if you are using the cloud and not doing anything particularly fancy the native tooling is good enough. For AWS that is DMS (for RDBMS) and Kinesis/Lamba (for streams). Google has Data Fusion and Dataflow . Azure hasData Factory if you are unfortunate enough to have to use SQL Server or Azure. Imo the vendored tools and open source tools are more useful when you need to ingest data from SaaS platforms, and... Source: over 3 years ago
This sub is for Apache Beam and Google Cloud Dataflow as the sidebar suggests. Source: almost 4 years ago
I am pretty sure they are using pub/sub with probably a Dataflow pipeline to process all that data. Source: almost 4 years ago
You can run a Dataflow job that copies the data directly from BQ into S3, though you'll have to run a job per table. This can be somewhat expensive to do. Source: almost 4 years ago
It was clear we needed something that was built specifically for our big-data SaaS requirements. Dataflow was our first idea, as the service is fully managed, highly scalable, fairly reliable and has a unified model for streaming & batch workloads. Sadly, the cost of this service was quite large. Secondly, at that moment in time, the service only accepted Java implementations, of which we had little knowledge... - Source: dev.to / over 4 years ago
Tell me what you think, also poke at it.. I have a bug list I'm addressing but could use more insights. https://besavee.com. Source: almost 4 years ago
Tell me what you think. https://besavee.com. Source: almost 4 years ago
Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.
Raindrop.io - All your articles, photos, video & content from web & apps in one place.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
AWS Snowball - AWS Snowball is a petabyte-scale data transport service that uses secure devices to transfer large amounts of data into and out of the AWS cloud.
Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.
Martechbase - A searchable database of 7,000+ marketing tools