BLOG different

Intel inside™. bi(OS) outside.
Posted on: . by Isima

Intel inside™. bi(OS) outside.

Authors: Mayur Kulkarni, Pradeep Madhavarapu, Isima with inputs from Ryan Metz, Rahul Unnikrishnan Nair, Intel Introduction In the early 90s, Intel ran a campaign1 about spotting the very best PCs.  This decade established Intel as the undisputed leader in microprocessors.  30 years later, engineers at Intel and Isima decided to attempt the same for data and analytics. Read on to learn more. Strategic focus “…to be competitive, …, in the cloud space, we need to bring a software-first focus” Pat Gelsinger CEO, Intel, CRN Under Pat’s leadership, Intel has embarked on a software-first strategy.  And there is no better software validation than analytics - the killer app of the cloud that also happens to consume the most effort (people and time), data-center space, and watts.  So Isima decided to validate what a converged offering can deliver with help from Intel ...

State of Multi-cloud Storage and Compute
Posted on: . by Pradeep Madhavarapu and Mayur Kulkarni

State of Multi-cloud Storage and Compute

Too Long, Must-read The [Live] Multi-cloud-native architecture can deliver a 10X better TCO than the other variants.  Some other unique insights we found -  Azure’s NVMe drives beat AWS and GCP by a significant amount w.r.to latencies. AWS delivered the most respectable IO ...

The last (and only) mile of resiliency you need. 10X cheaper.
Posted on: . by Naoki Iwakami

The last (and only) mile of resiliency you need. 10X cheaper.

Introduction What happens if the machine, an AZ, or a region serving your customers fails? Nothing - if you rely on cloud-native deployments, stateless microservices, and multi-master databases. That’s the promise anyway.  How many IT teams have the luxury of relying on multi-AZ ...

Scaling the multi-cloud Moon
Posted on: . by Pradeep Madhavarapu

Scaling the multi-cloud Moon

Introduction While the category leaders were busy drowning the market with deafening marketecture(s), our engineers landed on the multi-cloud ‘moon’ of Data and Analytics.  TL, DR - Ingest on AWS, Analyze on GCP and Visualize on Azure - in real-time, without migrating an ...

Taming the observability maze
Posted on: . by Naresh Sunkara

Taming the observability maze

Introduction Last week we wrote about how bi(OS) was hit with the load equivalent to two black Fridays on Thursday by tier-1 global retailers during Black Friday. While we are proud of our achievement, we don’t take our customers’ reliability for granted. Although, we do take a ...

Black Friday 2021 – A bi(OS) Perspective
Posted on: . by Darshan Rawal

Black Friday 2021 – A bi(OS) Perspective

Introduction Since bi(OS) serves unicorns and tier-1 retailers across three continents, we have a unique vantage point in experiencing Black Friday (and Cyber Monday), especially as it stresses Cloud Data Platforms.  This Black Friday saw bi(OS) handle 3x more peak load across all ...

Only Custom Silicon Can Beat Us
Posted on: . by Darshan Rawal

Only Custom Silicon Can Beat Us

Introduction Since the birth of SQL in the mid 70’s, there has been a wall of separation between OLTP and OLAP use cases. One of the defining characteristics of this separation is the stringent need for speed and QoS (quality of service) by OLTP applications. Even today, Data ...

Taming the non-deterministic Cloud
Posted on: . by Pradeep Madhavarapu and Naoki Iwakami

Taming the non-deterministic Cloud

The TL;DR summary Over a 12+ hour run, bi(OS)'s real-time OLAP engine delivered a p99 latency of 1.46ms for inserts, 2.94ms for selects at a peak throughput of 21.5K rows/sec with an 80:20 write: read split for 1KB rows when the system is 70%+ utilized. Writes followed ⅔ QUORUM ...

Who is M/F? Real World Data Quality
Posted on: . by Monish Suvarna

Who is M/F? Real World Data Quality

My blog last week talked about Schema lost in transit. Let me tell you a real story about a customer. We uploaded data from their repository into bi(OS) and the image above is a screenshot of what we saw on bi(OS) after an hour. We were not surprised as we have seen this story at ...

Schema lost in transit only to recreate again. WT*?
Posted on: . by Monish Suvarna

Schema lost in transit only to recreate again. WT*?

The current state of the art for Data Engineers is to build pipelines that ingest structured and semi-structured data in JSON, CSV, AVRO and store these as BLOBs on S3 (where the schema is lost). Then “Schema on Read” technologies such as Snowflake, Dremio or Presto process these ...