Webinar: Live Data Visualisation with Tableau and MongoDB
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The document discusses a webinar on integrating Tableau with MongoDB for live data visualization, highlighting features of MongoDB 3.2 and its BI connector. It explains how the connector translates SQL queries into MongoDB queries, allowing users to visualize multi-dimensional data without data transformation. The document encourages the audience to explore further resources, such as whitepapers and tools related to MongoDB and Tableau.
![Start a conversation. Tweet @MongoDB using #MongoDBWebinar
Optionally Manually Edit DRDL File
• Redact attributes
• Use more appropriate types
(sampling can get it wrong)
• Rename tables (v1.1+)
• Rename columns (v1.1+)
• Build new views using MongoDB
Aggregation Framework
– e.g., $lookup to join 2 tables
- table: homesales
collection: homeSales
pipeline: []
columns:
- name: _id
mongotype: bson.ObjectId
sqlname: _id
sqltype: varchar
- name: address.county
mongotype: string
sqlname: address_county
sqltype: varchar
- name:
address.nameOrNumber
mongotype: int
sqlname:
address_nameornumber
sqltype: varchar](https://image.slidesharecdn.com/mongodbbiconnectortableauwebinarv2-160113165520/85/Webinar-Live-Data-Visualisation-with-Tableau-and-MongoDB-9-320.jpg)
Webinar: Live Data Visualisation with Tableau and MongoDB
- 1. Start a conversation. Tweet @MongoDB using #MongoDBWebinar Live Data Visualization with Tableau and MongoDB David Spezia ([email protected]) Andrew Morgan ([email protected]) 13th January 2016
- 2. Start a conversation. Tweet @MongoDB using #MongoDBWebinar What is Tableau? Help people see and understand their data
- 3. Start a conversation. Tweet @MongoDB using #MongoDBWebinar Origins of Tableau
- 4. Start a conversation. Tweet @MongoDB using #MongoDBWebinar MongoDB + Tableau The power of Tableau + MongoDB + Analyze JSON data structures + Analyze data without transformation and ETL + Connect directly to and Visualize your data
- 5. Start a conversation. Tweet @MongoDB using #MongoDBWebinar MongoDB 3.2 – a BIG Release Hash-Based Sharding Roles Kerberos On-Prem Monitoring 2.2 2.4 2.6 3.0 3.2 Agg. Framework Location-Aware Sharding $out Index Intersection Text Search Field-Level Redaction LDAP & x509 Auditing Document Validation Fast Failover Simpler Scalability Aggregation ++ Encryption At Rest In-Memory Storage Engine BI Connector $lookup MongoDB Compass APM Integration Profiler Visualization Auto Index Builds Backups to File System Doc-Level Concurrency Compression Storage Engine API ≤50 replicas Auditing ++ Ops Manager
- 6. Start a conversation. Tweet @MongoDB using #MongoDBWebinar MongoDB Connector for BI Visualize and explore multi-dimensional documents using SQL-based BI tools. The connector does the following: • Provides the BI tool with the schema of the MongoDB collection to be visualized • Translates SQL statements issued by the BI tool into equivalent MongoDB queries that are sent to MongoDB for processing • Converts the results into the tabular format expected by the BI tool, which can then visualize the data based on user requirements
- 7. Start a conversation. Tweet @MongoDB using #MongoDBWebinar Location & Flow of Data MongoDB BI Connector Mapping meta-data Application data {name: “Andrew”, address: {street:… }} DocumentTableAnalytics & visualization
- 8. Start a conversation. Tweet @MongoDB using #MongoDBWebinar Defining Data Mapping mongodrdl --host 192.168.1.94 --port 27017 -d clusterdb -o clusterdb.drdl mongobischema import billy clusterdb.drdl DRDL mongodrdl mongobischema PostgreSQL MongoDB- specific Foreign Data Wrapper
- 9. Start a conversation. Tweet @MongoDB using #MongoDBWebinar Optionally Manually Edit DRDL File • Redact attributes • Use more appropriate types (sampling can get it wrong) • Rename tables (v1.1+) • Rename columns (v1.1+) • Build new views using MongoDB Aggregation Framework – e.g., $lookup to join 2 tables - table: homesales collection: homeSales pipeline: [] columns: - name: _id mongotype: bson.ObjectId sqlname: _id sqltype: varchar - name: address.county mongotype: string sqlname: address_county sqltype: varchar - name: address.nameOrNumber mongotype: int sqlname: address_nameornumber sqltype: varchar
- 10. Start a conversation. Tweet @MongoDB using #MongoDBWebinar Data, Data Every WhereDemo
- 11. Start a conversation. Tweet @MongoDB using #MongoDBWebinar Next Steps • Download the MongoDB 3.2 Whitepaper – https://www.mongodb.com/collateral/mongodb-3-2-whats-new • Try the MongoDB Connector for BI – https://www.mongodb.com/lp/download/mongodb-enterprise • Try Tableau Desktop with the MongoDB Connector for BI – http://www.tableau.com/products/desktop
Editor's Notes
- #3: Load slide: So why are were here? Because our mission is to: Click 1: …help people see and understand their data. It’s what we’re absolutely committed to because we know that giving everyone the power to unlock the answers buried in data - can transform the world.
- #4: Now that I’ve shown you how a little of how a company like Tableau might have used data visualization to help with their expansion plans, let’s take a look at where Tableau started out. The technology behind Tableau was started at Stanford University by Dr. Pat Hanrahan, who some of you might recognize as an early member of the company Pixar, and his Ph.D. student Dr. Chris Stolte. They teamed up with Christian Chabot, our CEO, and in 2003 they founded Tableau Software. The breakthrough technology they had developed was essentially the ability to translate drag-and-drop mouse actions into data queries that then are expressed visually. They called this Viz-Q-L. Viz-Q-L represents a foundational advancement in the area of data analysis and visualization. But why the focus on visual?
- #5: With MongoDB + Tableau you can: + Analyze JSON data structures + Analyze data without transformation and ETL + Connect directly to and Visualize your data
- #11: We have a data set of the Top Pubs from each of the London Burroughs provided from our friends at Yelp. I am sure you have heard of the Yelp Academic Challenge, they give out much of their JSON for free, and there is no better place to store and analyze that data than MongoDB + Tableau. Now let’s see Tableau + MongoDB in Action so we can make sure you…<next>