Which sql database service can scale to petabyte database sizes?

As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, Closed 11 years ago. One of my colleagues told me the other day SQL Server wasn't designed to handle Terabytes of data.....that could possibly be true for SQL 2000 or any DB 10 years ago, but I don't believe that to be the case today. How have others approached the situations where they need to store massive amounts of data (100 + Terabytes)? Growing one Single server is probably not the option, but I would think we could partition the data across many smaller servers and use views, etc to allow us to make one query call across the servers. Any idea how concurrency, etc performs in a model like this where data is Horizontally Partitioned across servers? Any suggestions / comments is greatly appreciated. Thanks, S

October, 2021 A distributed in-memory database guide Many databases claim today that they have the fastest, cheapest, and most scalable product on the market. Each comes to the market with new jargon and confusing statistics, making it hard for CTOs and CDOs to understand which products have the best foundations for high-performance and cost-effective products. This article highlights the critical technology considerations between the leading database vendors in plain English and why it matters when scaling towards petabytes. The aim is not to highlight the best product but rather the limitations and benefits of certain approaches. This article is not relevant to those dealing with sub-terabyte datasets. Vendor Landscape The database landscape is vast, with many sub-categories like graph, NoSQL, wide column, object-oriented, document, relational and more. Most database optimisations rely on generalisations around data structures; thus, no technology is able to achieve a one-size fits all. That said, the lines continue to blur, with some vendors like SingleStore combining relational and NoSQL functionality. This article will deal with large analytical database systems 1 designed for BI type queries. BI queries are unique in that the majority of queries look up very few columns and rows. For example, finding which products produced the highest profit margin last month. As data volumes continue to explode, many databases are struggling to keep up, and small differences in arc...

The petabyte is the new Interest in raw computational speed waned — sorry, IBM — after data center managers began turning away from super-expensive supercomputers and toward massive grids comprised of cheap PC servers. Meanwhile, the rise of business intelligence and its even more technical cousin, business analytics, has spurred interest in super-large data warehouses that boost profits by crunching the behavior patterns of millions of consumers at a time. Take Yahoo Inc.'s 2-petabyte, specially built data warehouse, which it uses to analyze the behavior of its half-billion Web visitors per month. The Sunnyvale, Calif.-based company makes a strong claim that it is not only the world's single-largest database, but also the busiest. Based on a heavily modified PostgreSQL engine, the year-old database processes 24 billion events a day, according to Waqar Hasan, vice president of engineering in Yahoo's data group. And the data, all of it constantly accessed and all of it stored in a structured, ready-to-crunch form, is expected to grow into the multiple tens of petabytes by next year. By comparison, large enterprise databases typically grow no larger than the tens of terabytes. Large databases about which much is publicly known include the EBay Inc. Even larger than the databases of Yahoo and eBay are the databases of the But Hasan noted that archived data is far different from live, constantly accessed data. "It's one thing to have data entombed; it's another to have it read...

How do you know if the next update to your software is ready for hundreds of millions of customers? It starts with data. And when it comes to Windows, we’re talking lots of data. The At Microsoft, the Windows diagnostic metrics are displayed on a real-time analytics dashboard called “Release Quality View” (RQV), which helps the internal “ship-room” team assess the quality of the customer experience before each new Windows update is released. Given the importance of Windows for Microsoft’s customers, the RQV analytics dashboard is a critical tool for Windows engineers, program managers, and execs. Not surprisingly, the real-time analytics dashboard is heavily used. “We have hundreds of active users every day, and thousands every month,” says Min Wei, principal engineer at Microsoft. “Delivering a new operating system update is like producing a Broadway show—there are so many people working behind the scenes to prepare. The RQV analytics dashboard helps ensure the curtain goes up on time—and that we deliver what the audience wants.” Figure 1: The internal RQV analytics dashboard at Microsoft helps the Windows team to assess the quality of upcoming Windows releases. The RQV dashboard tracks 20,000 diagnostic and quality metrics, and currently supports over 10 million queries per day, with hundreds of concurrent users. The RQV analytics dashboard relies on Postgres—along with the Citus extension to Postgres to scale out horizontally—and is deployed on Microsoft Azure. Two days...

• • AI & Machine Learning • API Management • Application Development • Application Modernization • Chrome Enterprise • Compute • Containers & Kubernetes • Data Analytics • Databases • DevOps & SRE • Maps & Geospatial • Security & Identity • Infrastructure • Infrastructure Modernization • Networking • Productivity & Collaboration • SAP on Google Cloud • Storage & Data Transfer • Sustainability • • IT Leaders • • Financial Services • Healthcare & Life Sciences • Manufacturing • Media & Entertainment • Public Sector • Retail • Supply Chain • Telecommunications • Partners • Startups & SMB • Training & Certifications • Inside Google Cloud • Google Cloud Next & Events • Google Maps Platform • Google Workspace • Developers & Practitioners • Transform with Google Cloud In early 2000s, Google developed Cloud Bigtable is available via a high-performance Companies such as Spotify, FIS, Energyworx and others are using Cloud Bigtable to address a wide array of use cases, for example: • Spotify has migrated its production monitoring system, • FIS is working on a bid for the SEC Consolidated Audit Trail (CAT) project, and was able to achieve • Energyworx is building an IoT solution for the energy industry on Google Cloud Platform, using Cloud Bigtable to store smart meter data. This allows it to scale without building a large DevOps team to manage its storage backend. Cloud Platform partners and customers enjoy the scalability, low latency and high throughput of Cloud Bigtable, without w...

As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, Closed 11 years ago. One of my colleagues told me the other day SQL Server wasn't designed to handle Terabytes of data.....that could possibly be true for SQL 2000 or any DB 10 years ago, but I don't believe that to be the case today. How have others approached the situations where they need to store massive amounts of data (100 + Terabytes)? Growing one Single server is probably not the option, but I would think we could partition the data across many smaller servers and use views, etc to allow us to make one query call across the servers. Any idea how concurrency, etc performs in a model like this where data is Horizontally Partitioned across servers? Any suggestions / comments is greatly appreciated. Thanks, S

October, 2021 A distributed in-memory database guide Many databases claim today that they have the fastest, cheapest, and most scalable product on the market. Each comes to the market with new jargon and confusing statistics, making it hard for CTOs and CDOs to understand which products have the best foundations for high-performance and cost-effective products. This article highlights the critical technology considerations between the leading database vendors in plain English and why it matters when scaling towards petabytes. The aim is not to highlight the best product but rather the limitations and benefits of certain approaches. This article is not relevant to those dealing with sub-terabyte datasets. Vendor Landscape The database landscape is vast, with many sub-categories like graph, NoSQL, wide column, object-oriented, document, relational and more. Most database optimisations rely on generalisations around data structures; thus, no technology is able to achieve a one-size fits all. That said, the lines continue to blur, with some vendors like SingleStore combining relational and NoSQL functionality. This article will deal with large analytical database systems 1 designed for BI type queries. BI queries are unique in that the majority of queries look up very few columns and rows. For example, finding which products produced the highest profit margin last month. As data volumes continue to explode, many databases are struggling to keep up, and small differences in arc...

The petabyte is the new Interest in raw computational speed waned — sorry, IBM — after data center managers began turning away from super-expensive supercomputers and toward massive grids comprised of cheap PC servers. Meanwhile, the rise of business intelligence and its even more technical cousin, business analytics, has spurred interest in super-large data warehouses that boost profits by crunching the behavior patterns of millions of consumers at a time. Take Yahoo Inc.'s 2-petabyte, specially built data warehouse, which it uses to analyze the behavior of its half-billion Web visitors per month. The Sunnyvale, Calif.-based company makes a strong claim that it is not only the world's single-largest database, but also the busiest. Based on a heavily modified PostgreSQL engine, the year-old database processes 24 billion events a day, according to Waqar Hasan, vice president of engineering in Yahoo's data group. And the data, all of it constantly accessed and all of it stored in a structured, ready-to-crunch form, is expected to grow into the multiple tens of petabytes by next year. By comparison, large enterprise databases typically grow no larger than the tens of terabytes. Large databases about which much is publicly known include the EBay Inc. Even larger than the databases of Yahoo and eBay are the databases of the But Hasan noted that archived data is far different from live, constantly accessed data. "It's one thing to have data entombed; it's another to have it read...

• • AI & Machine Learning • API Management • Application Development • Application Modernization • Chrome Enterprise • Compute • Containers & Kubernetes • Data Analytics • Databases • DevOps & SRE • Maps & Geospatial • Security & Identity • Infrastructure • Infrastructure Modernization • Networking • Productivity & Collaboration • SAP on Google Cloud • Storage & Data Transfer • Sustainability • • IT Leaders • • Financial Services • Healthcare & Life Sciences • Manufacturing • Media & Entertainment • Public Sector • Retail • Supply Chain • Telecommunications • Partners • Startups & SMB • Training & Certifications • Inside Google Cloud • Google Cloud Next & Events • Google Maps Platform • Google Workspace • Developers & Practitioners • Transform with Google Cloud In early 2000s, Google developed Cloud Bigtable is available via a high-performance Companies such as Spotify, FIS, Energyworx and others are using Cloud Bigtable to address a wide array of use cases, for example: • Spotify has migrated its production monitoring system, • FIS is working on a bid for the SEC Consolidated Audit Trail (CAT) project, and was able to achieve • Energyworx is building an IoT solution for the energy industry on Google Cloud Platform, using Cloud Bigtable to store smart meter data. This allows it to scale without building a large DevOps team to manage its storage backend. Cloud Platform partners and customers enjoy the scalability, low latency and high throughput of Cloud Bigtable, without w...

How do you know if the next update to your software is ready for hundreds of millions of customers? It starts with data. And when it comes to Windows, we’re talking lots of data. The At Microsoft, the Windows diagnostic metrics are displayed on a real-time analytics dashboard called “Release Quality View” (RQV), which helps the internal “ship-room” team assess the quality of the customer experience before each new Windows update is released. Given the importance of Windows for Microsoft’s customers, the RQV analytics dashboard is a critical tool for Windows engineers, program managers, and execs. Not surprisingly, the real-time analytics dashboard is heavily used. “We have hundreds of active users every day, and thousands every month,” says Min Wei, principal engineer at Microsoft. “Delivering a new operating system update is like producing a Broadway show—there are so many people working behind the scenes to prepare. The RQV analytics dashboard helps ensure the curtain goes up on time—and that we deliver what the audience wants.” Figure 1: The internal RQV analytics dashboard at Microsoft helps the Windows team to assess the quality of upcoming Windows releases. The RQV dashboard tracks 20,000 diagnostic and quality metrics, and currently supports over 10 million queries per day, with hundreds of concurrent users. The RQV analytics dashboard relies on Postgres—along with the Citus extension to Postgres to scale out horizontally—and is deployed on Microsoft Azure. Two days...