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The new
Data Economics
Founder & CEO of TileDB, Inc.
Dr. Stavros Papadopoulos
2021
Data Economics
Consumption
How tools can compute
on the data, where
does the computation
happen
Distribution
Who has access to the
data, what is the means
of access, and
monetization
Production
What format does the
data get produced in
and where does it get
stored
The Problem
Distribution (sharing & collaboration) is an afterthought
Data produced in inefficient formats
Most formats are domain-specific
Tools mostly domain-specific
A lot of ETL involved for
generic tools (e.g., DBs)
All data management
solutions focus here
Consumption
How tools can
compute on the data,
where does the
computation happen
Examples
Data in CSV files
Storage in cloud buckets
Marine traffic (AIS)
Dropbox-like solutions for secure sharing
Analysis with tools like bcftools, GATK
Genomics
Data in FastQ, BAM, VCF formats
Distributed / sold as files
Storage in cloud buckets
Distributed / sold as files
Finance (historical market data)
Data in text files
Storage in cloud buckets
Distributed as files with metadata
LiDAR
Data in LAS files
Storage in cloud buckets or FTP servers
Analysis / visualization with GIS tools
Analysis with DB and data science tools Analysis / visualization with GIS tools
The Production Problem
Data in some
custom format
slow & expensive
often custom & in-house
costly & time consuming
Some analytics
infra
Extract - Transform -
Load (ETL)
Storage in some cloud
bucket or file manager
The Distribution Problem #1
Data in some
custom format
wasteful re-invention
Storage in some cloud
bucket or marketplace Org #N:
Download + Wrangle +
Built analytics infra
Org #1:
Download + Wrangle +
Built analytics infra
The Distribution Problem #2
Data in some
custom format
Data owner bears the distribution cost,
also re-invention across application domains
ETL, etc. some analytics infra
Queries by
consumer #1
Queries by
consumer #N
The Consumption Problem
Data in some
custom format
inefficient & costly
with poor governance
Storage in some cloud
bucket or server
Group #N:
Wrangle + Copy - Use tool & infra #N
Group #1:
Wrangle + Copy - Use tool & infra #1
The Solution
No ETL, no copies
Common for all data applications
Unified governance
Built-in marketplace
One infra, any backend, any scale
Universal
data management platform
Data in a universal,
analysis-ready format
User / group #1:
any tool, any scale
User / group #N:
any tool, any scale
Enter TileDB
Secure governance & collaboration
Scalable, serverless compute
Data & code sharing & monetization
Pay-as-you-go, consumer pays
Extreme interoperability
No infra hassles
multi-dimensional arrays
Universal data
management platform
Data in a universal,
analysis-ready format
User / group #1:
any tool, any scale
User / group #N:
any tool, any scale
The Secret Sauce | The Data Model
Dense array
Store everything as dense or sparse multi-dimensional arrays
Sparse array
TileDB Cloud cloud.tiledb.com
❏ Access control and logging
❏ Serverless SQL, UDFs, task graphs
❏ Jupyter notebooks and dashboards
Unified data management
and easy serverless compute
at global scale
How we built a Universal Database
Pluggable Compute: Efficient APIs & Tool Integrations
TileDB Embedded github.com/TileDB-Inc/TileDB
Open-source interoperable
storage with a universal
open-spec array format
❏ Parallel IO, rapid reads & writes
❏ Columnar, cloud-optimized
❏ Data versioning & time traveling
Deep roots at the intersection of HPC, databases and data science
Traction with telecoms, pharmas, hospitals and other scientific organizations
40 members with expertise across all applications and domains
Who we are
TileDB got spun out from MIT and Intel Labs in 2017
WHERE IT ALL STARTED
Raised over $20M, we are very well capitalized
INVESTORS
The Universal Database
Thank you
WE ARE HIRING
https://apply.workable.com/tiledb/

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The New Data Economics

  • 1. The new Data Economics Founder & CEO of TileDB, Inc. Dr. Stavros Papadopoulos 2021
  • 2. Data Economics Consumption How tools can compute on the data, where does the computation happen Distribution Who has access to the data, what is the means of access, and monetization Production What format does the data get produced in and where does it get stored
  • 3. The Problem Distribution (sharing & collaboration) is an afterthought Data produced in inefficient formats Most formats are domain-specific Tools mostly domain-specific A lot of ETL involved for generic tools (e.g., DBs) All data management solutions focus here Consumption How tools can compute on the data, where does the computation happen
  • 4. Examples Data in CSV files Storage in cloud buckets Marine traffic (AIS) Dropbox-like solutions for secure sharing Analysis with tools like bcftools, GATK Genomics Data in FastQ, BAM, VCF formats Distributed / sold as files Storage in cloud buckets Distributed / sold as files Finance (historical market data) Data in text files Storage in cloud buckets Distributed as files with metadata LiDAR Data in LAS files Storage in cloud buckets or FTP servers Analysis / visualization with GIS tools Analysis with DB and data science tools Analysis / visualization with GIS tools
  • 5. The Production Problem Data in some custom format slow & expensive often custom & in-house costly & time consuming Some analytics infra Extract - Transform - Load (ETL) Storage in some cloud bucket or file manager
  • 6. The Distribution Problem #1 Data in some custom format wasteful re-invention Storage in some cloud bucket or marketplace Org #N: Download + Wrangle + Built analytics infra Org #1: Download + Wrangle + Built analytics infra
  • 7. The Distribution Problem #2 Data in some custom format Data owner bears the distribution cost, also re-invention across application domains ETL, etc. some analytics infra Queries by consumer #1 Queries by consumer #N
  • 8. The Consumption Problem Data in some custom format inefficient & costly with poor governance Storage in some cloud bucket or server Group #N: Wrangle + Copy - Use tool & infra #N Group #1: Wrangle + Copy - Use tool & infra #1
  • 9. The Solution No ETL, no copies Common for all data applications Unified governance Built-in marketplace One infra, any backend, any scale Universal data management platform Data in a universal, analysis-ready format User / group #1: any tool, any scale User / group #N: any tool, any scale
  • 10. Enter TileDB Secure governance & collaboration Scalable, serverless compute Data & code sharing & monetization Pay-as-you-go, consumer pays Extreme interoperability No infra hassles multi-dimensional arrays Universal data management platform Data in a universal, analysis-ready format User / group #1: any tool, any scale User / group #N: any tool, any scale
  • 11. The Secret Sauce | The Data Model Dense array Store everything as dense or sparse multi-dimensional arrays Sparse array
  • 12. TileDB Cloud cloud.tiledb.com ❏ Access control and logging ❏ Serverless SQL, UDFs, task graphs ❏ Jupyter notebooks and dashboards Unified data management and easy serverless compute at global scale How we built a Universal Database Pluggable Compute: Efficient APIs & Tool Integrations TileDB Embedded github.com/TileDB-Inc/TileDB Open-source interoperable storage with a universal open-spec array format ❏ Parallel IO, rapid reads & writes ❏ Columnar, cloud-optimized ❏ Data versioning & time traveling
  • 13. Deep roots at the intersection of HPC, databases and data science Traction with telecoms, pharmas, hospitals and other scientific organizations 40 members with expertise across all applications and domains Who we are TileDB got spun out from MIT and Intel Labs in 2017 WHERE IT ALL STARTED Raised over $20M, we are very well capitalized INVESTORS
  • 14. The Universal Database Thank you WE ARE HIRING https://apply.workable.com/tiledb/