The diversity of data management systems affords developers the luxury of building heterogeneous architectures to address the unique needs of big data. It allows one to mix-n-match systems that can store, query, update, and process data based on specific use cases. However, this heterogeneity brings with it the burden of developing custom interfaces for each data management system. Existing big data frameworks fall short in mitigating these challenges imposed. In this paper, we present Bindaas, a secure and extensible big data middleware that offers uniform access to diverse data sources. By providing a RESTful web service interface to the data sources, Bindaas exposes query, update, store, and delete functionality of the data sources as data service APIs, while providing turn-key support for standard operations involving access control and audit-trails. The research community has deployed Bindaas in various production environments in healthcare. Our evaluations highlight the efficiency of Bindaas in serving concurrent requests to data source instances with minimal overheads.

1. Data Services with Bindaas:
RESTful Interfaces for Diverse Data Sources
Pradeeban Kathiravelu1
, Yusuf Nadir Saghar2
, Tushar Aggarwal3
, Ashish Sharma1
1
Emory University, 2
Vanderbilt University, 3
BITS Pilani
IEEE Big Data 2019
Los Angeles, CA, USA. Dec 11th
, 2019.

2. Introduction
● Diverse needs for big data storage and processing.
○ Heterogeneous data management frameworks.
● Custom interfaces of data management frameworks.
○ Interoperability?
○ Integration?
● Interacting with multiple big data frameworks.
○ How to minimize the need for custom-code for each? 2

3. Motivation
● Data Services - Web Services Approach for Big Data
○ Standard web service interfaces for big data access and
processing.
○ Implementation burden should not be on the application
developer.
3

4. Our Proposal: Bindaas
● An extensible middleware platform
○ for diverse data management frameworks.
4

5. Bindaas powers the API Layers for:
● The Cancer Imaging Archive
(30TB of images + metadata).
● TCIA web image viewer
● caMicroscope where a user
examines >1GB high-res
pathology images and
segmentation overlays (~5K
JSON docs/sec)
● Real-time prediction of Sepsis.5

6. Bindaas
● A middleware to integrate data sources seamlessly.
● Modular (OSGi-based) open source Java framework.
○ Extensible and secure
6

7. RESTful
Interfaces to Data
Sources.
● Datasource configured as
shareable projects.
● Relational
○ MySQL, Postgres, DB2
● NoSQL
○ Mongo, ..
● Others
○ Apache Drill, HTTP, ..
7

8. Bindaas Workflow
Query/Payload Modifiers and Query Results Modifiers
8

9. Audit Trails and Access Control
● Authentication based on API keys and JWT tokens.
● Unified authentication from third-party identity providers.
○ Using Auth0 Identity Infrastructure as a Service framework
9

10. Evaluation
● MySQL/Mongo + Bindaas: The data source server
● Airbnb data (56MB) to create sample database.
● Concurrent database queries: The client
○ Negligible overhead accessing directly vs. via Bindaas.
● Perform operations on Mongo/Bindaas:
○ Count (number of hosts with profile picture)
○ Distinct (distinct hosts that fit a certain criteria) 10

11. Response time
11
● The server in an AWS cloud VM.
● The client in a local server.
count distinct

12. Conclusion
● Bindaas, RESTful interfaces to various data sources.
■ Relational, NoSQL, and non-traditional.
● Bindaas as a backbone for several healthcare
research systems such as TCIA.
Future work
● More datasource providers
● Faster data serializations with Protobuf
12
Thank you! Questions?