This document discusses how Dremio provides a unified access point for data across an entire organization. It summarizes how Dremio allows various users, including data engineers, scientists, analysts and business users, to access all kinds of data sources through SQL or REST APIs. Dremio also enables features like data catalogs, collaborative workspaces, and workload monitoring that help organizations better manage and govern their data.

1. All my Data Available to My Organization,
or Even My Ecosystem, Very Simply
Charly CLAIRMONT
CTO, Synaltic
cclairmont@synaltic.fr
@egwada

2. Charly CLAIRMONT
CTO, Synaltic
@egwada
Data Architect, Data Engineer
Help customers build data
management platform
Hadoop User Group, Paris Spark
Meetup Cofounder

3. Heterogeneous
data sources
Data Warehouse
Data Lake
Data Hub
Expertise Data Analysis
Data Science
Data Strategy
15 years old
35 #SynalTeam
members
Datavisualisation
#DataIntelligence , #DataOps
On Premise,
Cloud,
Hybrid cloud !
Synaltic
API
Data Engineering
Ingest
ETL
Streaming
Scheduler

4. In 2009 Altic promoted Teiid, an open source database virtualisation platform
From database virtualisation...

5. Core Hadoop
HDFS, MR
2006
Hbase
Zookeeper
Core Hadoop
2008
Hive
Pig
Mahout
Hbase
Zookeeper
Core Hadoop
2009
Sqoop
whirr
Avro
Hive
Pig
Mahout
Hbase
Zookeeper
Core Hadoop
2010
Flume
Bigtop
Oozie
MRUnit
HCatalog
Sqoop
whirr
Avro
Hive
Pig
Mahout
Hbase
Zookeeper
Core Hadoop
2011
Spark
Impala
Solr
Kafka
Flume
Bigtop
Oozie
MRUnit
HCatalog
Sqoop
whirr
Avro
Hive
Pig
Mahout
Hbase
Zookeeper
Core Hadoop
2012
Nifi
Flink
Atlas
Ranger
Drill
Parquet
Sentry
ORC
Spark
Impala
Solr
Kafka
Flume
Bigtop
Oozie
MRUnit
HCatalog
Sqoop
whirr
Avro
Hive
Pig
Mahout
Hbase
Zookeeper
Core Hadoop
2015
Submariner
Ozone
PrestoDB
Dremio
Alluxio
Beam
Calcite
Nifi
Flink
Atlas
Ranger
Drill
Parquet
Sentry
Spark
Impala
Solr
Kafka
Flume
Bigtop
Oozie
MRUnit
HCatalog
Sqoop
whirr
Avro
Hive
Pig
Mahout
Hbase
Zookeeper
Core Hadoop
Aujourd’hui
From files to Cloud Data Warehouse
A slow evolution of data management

6. From files to Cloud Data Warehouse
So now you can build your own data warehouse with Hadoop

7. From files to Cloud Data Warehouse
So many projects to build yours

8. Facebook, Google, Uber,
Airbnb… transformed
end users experience.
That why analytical
platforms need such
accessibility and
simplicity.
In the same time end users need simplicity !

9. Let end users do by
themselves!
Self Service is now the motto !

10. Data is distributed in all organisations!
The Next Wave: Data, AI and Analytics in 2019 and Beyond
Matt Aslett, Research Vice President

11. How to Federate Data?
Is there a way to have one unique point to access
data across all the organisation?

12. Dremio changes the game !
● Founded in 2015
● Based on
○ Apache Parquet,
Apache Calcite et
Apache Arrow
○ Version 1.0 en 2017
○ Actuellement en
version 3.2

13. All kind of data sources !

14. The deconstructed database interchange: Apache Arrow
How does Dremio work?

15. SQL, API REST, or both

16. JDBC, ODBC so you could use your classic business intelligence tools

17. A collaborative approach of data management (1/2)
”data catalog”

18. A collaborative approach of data management (2/2)
« shared data workspace »

19. With Dremio you can scale when you need:
from one node to cluster
Hadoop Yarn Kubernetes

20. Dremio, boost your data access with “Reflection”
efficient cache management

21. Dremio provides a lineage to understand “how queries are built”

22. Audit how Dremio is used
”traceability of workloads”

23. USE CASES

24. A unique access point to data in an organisation
A newspaper use case
Collect, store, clean, organise and manage data
to obtain a 360° overview of customer.
● Allow a secured, centralised and
collaborative data access for all users :
● data engineers
● data scientist
● data analyst
● business users
Compute and storage separation approach
Start ephemeral cluster if needed

25. Audit & Monitoring workloads
Although one of Dremio's promises is performance, our real interest is the ability to secure the
user access to all our data sources, and track each user's workload in R, Spark, Python, Odbc,
Jdbc...
A major actor in the energy market

26. DEMO

27. Thanks !
Questions?
@egwada / @Synaltic