SQL Server Big Data Clusters allow for data exploration and analysis using SQL Server, Spark, HDFS, and Kubernetes. Key features include deploying clusters on any Kubernetes environment, management services for monitoring and high availability, and Polybase for querying external data stores via SQL. The architecture consists of compute, storage, and SQL pools distributed across SQL Server instances managed by Kubernetes. Azure Data Studio provides tools for working with relational and HDFS data on the clusters.

1. SQL Server
Big Data Clusters
Rock Pereira
SQL Saturday, Redmond
April 27, 2019

2. Contents
1.Kubernetes for Data Science
2.SQL Server Big Data Clusters
3.Understand the problem
4.Data exploration and analysis
5.Data-driven application development with Kubernetes

3. 1 Kubernetes for Data Science

4. 1.1 What is Kubernetes?

5. 1.1 What is Kubernetes?
Docker Containers
MCR:
Microsoft Container
Registry

6. 1.2 Benefits
Build &
Configure
InsightObservation
Estimate
Compute Needs
Parameterized
Deployment
Autoscaling

7. 1.3 Team Data Science Lifecycle

8. 1.4 Demo: SS 2019 in Minikube

9. 2 SQL Server Big Data Clusters

10. 2.1 What is a Big Data Cluster?
Unified data platform for analytics
Data-driven solutions using Kubernetes
Components of a BDC:
●
Spark - Distributed, In-memory compute
●
HDFS - Elastic Storage
●
SQL Server - Data Hub for structured &
unstructured data
●
Kubernetes - Scale-out, fault-tolerant

11. 2.2 Features
●
Deploy anywhere there is managed Kubernetes
●
Management services for logging, monitoring,
backup and high availability
●
Consistent portal for managing all your clusters

12. 2.3 Polybase
Query HDFS (Azure Blob Storage, Hortonworks, Cloudera)
using External Tables in SQL Server
●
Manage permissions with Active Directory
●
No data duplication – The data is not persistent
New in SQL Server 2019:
●
Connectors to Azure SQL DB, Azure SQL DW, Oracle,
Teradata, MongoDB, Azure CosmosDB + any ODBC
compliant source with an ODBC driver (IBM DB2, SAP
Hana, Microsoft Excel)
●
Read CSV & parquet

13. 2.4 Architecture
Compute Pool:
Parallel Ingest
Storage Pool:
Scalable Storage
Data Processing
SQL Data Pool:
Caching External Data
Distributed across SS
Instances
SS Master Pool:
Read-Write OLTP
Store dimensional

14. 2.5 Azure Data Studio
●
Work with relational (big) data in SQL Server
●
HDFS browser – like Azure Storage Explorer
●
External Table wizard, incl column mapping
●
Jupyter-based notebooks
●
Collaboration
●
Code with intellisense
●
Submit Spark jobs

15. 2.6 Deploying a Big Data Cluster
Minikube On-Prem Cloud (AKS)
Single Node
Requirements:
Memory: 32 GB
CPU: 8
Disk Space: 100 GB
Use kubeadm Use python script
Set environment
variables before
deploying
Tools:
mssqlctl (app_commands, ref), kubectl, Azure CLI
Azure Data Studio + SQL Server 2019 extension