Pravjit Tiwana

Catch our top Solutions Architects Sri and Rita in this session at AWS Innovate. Learn the latest approaches to rapidly modernize your .NET Applications using GenAI and the power of the AWS Cloud.

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Amazon RDS Multi-AZ deployment with two readable standbys supports 6 additional AWS Regions Three-node Multi-AZ DB Cluster is now available in Asia Pacific (Hyderabad), Asia Pacific (Melbourne), Europe (Spain), Europe (Zurich), Middle East (UAE), and Israel (Tel Aviv) Regions. This deployment option supports minor version upgrades and system maintenance updates with typically less than one second of downtime when using Amazon RDS Proxy or any one of the open-source AWS Advanced JDBC Driver, PgBouncer, or ProxySQL. https://lnkd.in/g9D9RGVB #Amazon #RDS #PostgreSQL #MySQL

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Data marts, warehouses, lakes, whatever the name, the pattern of consolidating data across an enterprise for comprehensive analytics is a familiar one. Every enterprise has many data sources, some operational, some analytics, some telemetry, and bringing them together has always been a challenge. Cleaning the data, curating for consistency, exploring, doing data science, building reports, monitoring, is another series of challenges in bringing a range of tools to bear on the data, thanks to diverse data formats and tooling. With Gen AI, the requirements (e.g., vector indexing for RAG) and potential value of the data have both gone up. And the pressure to ensure right use of the data, to comply to a broad range of regulations, to secure the data has never been higher. We believe the time has come to come together around open data standards to ensure that data is easy to consolidate and easy to plug into a wide variety of tools, all in a properly governed way. Josh and I describe the approach we are taking at Microsoft with Fabric and Purview. We are building on open Parquet-based updatable table formats such as Delta, Iceberg and Hudi, and interop across them with XTable. We are making it easy to consolidate data with mechanisms like mirroring and shortcuts. Ultimately, users should be able to use their tool of choice for a given task with the least friction.

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Analyzing #metadata about #data tells a fantastic story and we in #gcs have made that really simple with #genai powered analytics at your fingertips

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Sometimes small changes are a big deal. S3's per-account bucket limit has meant that buckets have been more of a human provisioning thing, starkly different from objects, which tend to be interacted with primarily from code. For years, customers have been asking us to increase this limit -- and in the storage innovation talk at re:Invent this year, i'd tell a bit of a story about why it was a more complicated bit of work than you might expect. With this limit increase, buckets have become a much more programmatic construct. We hear customers talking about using buckets, with these limit increases, as a way of managing data sharing with their own customers and using them to more effectively isolate data for specific jobs and teams. If larger numbers of buckets opens up a new pattern for you, I'd love to hear more about it. These launches, like object consistency, S3 conditionals, and big bucket limit increases have a tendency to fly a little below radar. But they are the ones that I actually hear the most excitement back from developers about, because they really broaden S3 as a programmatic interface to your data, and a building block for really incredible systems. I'm so impressed with the hard and careful work on the team that went in to getting this one out. https://lnkd.in/gA2cWATM

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Folks I've spoken to about Amazon have likely heard me talk about the importance of the PR/FAQ process, a.k.a. "the working backwards process". This blog posts gives a very nice example of a PR/FAQ. https://lnkd.in/edTeuVRQ. Note that the blog doesn't capture the presumably 100s of reviews/discussions/rewrites that this doc represents, which is a part of the real value. When the process is done it all looks so simple, but getting a large organization to make a decision to something simple and do it in a BIG way is the challenge the fun of it all.

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Amazon Web Services (AWS) rolled out v1.0 of cost data export compliant with FinOps Foundation FOCUS specifications. zOpt.ai is a contributor to #FinOps #FOCUS specifications and I am glad to see the progress and adoption of the standard specifications by all the major cloud providers. This is a major step in understanding cloud spend across cloud providers in uniform manner.

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New Episode: 268: Long Time Show Host is CloudPod’s first Casualty to AI (For This Week, at Least) https://bit.ly/4bURA59 Episode 268 of The Cloud Pod podcast, bringing you all the latest news from Cloud and AI with AWS, Azure, Google, OCI, OpenSSH, CloudFlare, and Lambda #aws #gcp #azure #cloud #cloudpodcast #podcast

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New Episode: 260: Amazon Dispatches AWS CEO Adam Selipsky with Prime 2-day delivery Episode 260 of The Cloud Pod podcast, bringing you all the latest news from Cloud and AI including AWS, Azure, Google, OCI, GPT-4.o, C7i-flex, AWS CEO Changes and Amazon Polly #aws #gcp #azure #cloud #cloudpodcast #podcast

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A core tenet for #AWS has always been that, if it was possible to take care of scaling, resilience, availability, security, and cost management of our customer's infrastructure components, we should do so. Eventually, this became known as "serverless", which encompasses much more than executing code without the need for servers. Amazon SQS and S3 were the first AWS services launched, and they were "serverless" from day one. A good example of this philosophy in practice is Aurora Serverless, which provides on-demand auto-scaling for Amazon Aurora databases. It proactively scales up resources during peak periods to ensure the predictable performance when you require it. When demand subsides, it seamlessly scales back down, reducing waste. And with granular, second-by-second billing, you can truly optimize for cost. Remember, the essence of frugal architecture is robust monitoring driving the ability to optimize costs. To dive deeper into how Aurora Serverless exemplifies this principle, I recommend reading the latest paper here: https://lnkd.in/e4uPK6wv #AWS #serverless #databases

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It was just yesterday that we saw GitHub announce a Copilot Workspace Tech Preview. Now Amazon comes out swinging with what looks like a pretty serious entry into the AI coding assistance space. Will be diving into this one in more depth in the coming days. #AWS #AIDev #GenAI

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Citius ! Loyal Guru got 2x faster with AlloyDB for PostgreSQL. "Prior to adopting AlloyDB, large traffic spikes from major retail events on Loyal Guru often created bottlenecks. We struggled to deal with data growth, which strained the limits of our storage capacity. AlloyDB's autopilot feature for managing storage has been instrumental in mitigating these issues, automatically adjusting storage capacity to meet our demands without manual intervention. Overall, we reduced query latency by 40-50% and optimized storage from 2TB to 1.3TB — a 35% improvement." #alloydb #gcp #postgresql

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