SlideShare una empresa de Scribd logo
1 de 17
Implementing
Salesforce Big Objects
Jigar Shah, Eternus Solutions, Enterprise Architect
@jigarshah189 /in/jigarshah189
Agenda 2
Need for Big Objects
What is a Big Object?
Big Object Use Cases
Considerations for Usage
Demo
Q & A
Need for Big
Objects?
3
Nature of Storage Performance Cost
• Master Data
• Business Data
• Operational Data
• Performance
diminishes with
large data sets
• Data retrieval limits
• Limited Data
Storage
What is a Big Object? 4
Object that stores & manages
massive data volumes
within Salesforce without
affecting performance.
▶ Does NOT count against org data
storage limits
▶ Processing scale of 1 billion records
▶ Types
 Standard (FieldHistoryArchive)
 User Defined
 Suffixed with “__b”
Big Object Use Cases 5
CAPTURE USER
ACTIVITY
Code reviews, time
entries, page views,
field audits etc.
RETAIN HISTORICAL
DATA
Historical data stored
for compliance
360 CUSTOMER VIEW
Ancillary customer
data e.g. Purchase
Details, Transactions
Considerations for Big Objects Usage 6
General
UI/ UX Data Security
& Access
Analytics Packaging
• Metadata API
• Max. 100 Big Objects per org
• Supports DateTime, Lookup, Number, Text, Long Text Area field
types only
• Triggers, Flows, Processes, Salesforce App are unavailable
• Async SOQL is restricted to specific licenses
• Standard UI unavailable (Tabs, Detail
Pages, List Views)
• Works with Visualforce Pages or
Lightning Components
• Supports Object & Field
Permissions only
• Included in Managed Packages
• No support for Report Builder
• Einstein Analytics supported
Demo 7
• Use Case
• Big Objects Schema Definition
• Big Object Record Creation
• Data Retrieval
• Standard SOQL
• Async Soql
Demo – Use Case 8
• Extreme Gaming is globally renowned provider of online arcade games. They have an
extremely popular game which has thousands of online players.
• This company intends to store all the interactions the players make in a single play of the
game within Salesforce.
• The game has numerous interactions per day which multiplied with its huge set of players
results in tons of data.
Object Definition 9
Customer Interactions (Customer_Interaction__b)
# Field Label Field Name Required? Type Indexed Order
1 In-Game Purchase In_Game_Purchase__c Text (16)
2 Level Achieved Level_Achieved__c Text (16)
3 Lives Used Lives_This_Game__c Text (16)
4 Game Platform Game_Platform__c Yes Text (16) ASC 2
5 Score This Game Score_This_Game__c Text(16)
6 User Account Account__c Yes Lookup (Account) DESC 1
7 Date of Play Play_Date__c Yes DateTime DESC 3
8 Play_Duration__c Play_Duration__c Yes Number (18, 2)
Deploying your Schema 10
SchemaDefinition
Package.xml
Metadata Type
Object File
Object Definition
(Name, Label, Fields)
Indexes
Permissions File Profile or Permission Set Access
Big Object Data Manipulation 11
• Apex CRUD
• Create / Update (Idempotent Behavior)
• insertImmediate(sobject) OR insertImmediate(sobjects)
• Read
• SOQL Queries
• Async SOQL
• CSV Files
• API (Bulk API, SOAP API)
Using Standard SOQL with Big Objects 12
Executes
synchronously
All Indexes are
mandatory
Comparison
Operators
(=, <, >, <=, >=, IN)
Not Supported
Operators
(!=, LIKE, NOT IN, EXCLUDES, INCLUDES)
Using Async SOQL with Big Objects 13
{
"jobId":"08PD000000003kiT",
"message":"",
"query":"SELECT Account__c, In_Game_Purchase__c FROM Customer_Interaction__b WHERE Play_Date__c='2018-
01-05'",
"status":"New",
"targetObject":"Customer_Interaction_Analysis__c",
"targetFieldMap":{
"Account__c":"Account__c",
"In_Game_Purchase__c":"Purchase__c"
},
"targetValueMap":{
"$JOB_ID":"BackgroundOperationLookup__c",
"Copy fields from source to target":"BackgroundOperationDescription__c"
}
}
SOQL Vs Async SOQL Usage Considerations 14
Feature Standard SOQL Async SOQL
Mode of Execution Synchronous Asynchronous
Immediate Response Required? Yes No
Expected Result Set Size Smaller Data Sets (Thousands of records) Large Data Sets (Millions of records)
Best Suited For
• Displaying Data on UI
• Manipulations within Apex
• Aggregation
• Summarizing for Analytics
Filter using Non Index fields Yes No
Sample Format
SELECT Game_Platform__c, Play_Date__c
FROM Customer_Interaction__b
WHERE
Game_Platform__c='PC' AND Play_Date__c='2017-09-06'
{
"query": "SELECT Account_c, In_Game_Purchase__c FROM Customer_Interaction__b
WHERE Play_Date__c='2017-09-06'",
"operation": "insert",
"targetObject": "Customer_Interaction_Analysis__c",
"targetFieldMap": {
"Account__c":"Account__c",
"In_Game_Purchase__c":"Purchase__c"
},
"targetValueMap":{
"$JOB_ID“ : "BackgroundOperationLookup__c",
"Copy fields from source to target“ : "BackgroundOperationDescription__c"}
}
Additional References 15
 Big Object Basics (Trailhead Module)
 Big Objects – Bring Data to Force.com (YouTube)
Big Objects Implementation Guide (Salesforce Documentation)
16
Questions?
Thank You
https://twitter.com/EternusCPQ
https://www.facebook.com/ecpq
https://www.eternussolutions.com/
https://www.linkedin.com/company/eternus-solutions-private-limited/

Más contenido relacionado

La actualidad más candente

Azure data catalog your data your way eugene polonichko dataconf 21 04 18
Azure data catalog your data your way eugene polonichko dataconf 21 04 18Azure data catalog your data your way eugene polonichko dataconf 21 04 18
Azure data catalog your data your way eugene polonichko dataconf 21 04 18Olga Zinkevych
 
PatSeer Lite Overview
PatSeer Lite OverviewPatSeer Lite Overview
PatSeer Lite OverviewGridlogics
 
Orbit Patent Search
Orbit   Patent SearchOrbit   Patent Search
Orbit Patent SearchNurjahan M
 
Intro to new Google cloud technologies: Google Storage, Prediction API, BigQuery
Intro to new Google cloud technologies: Google Storage, Prediction API, BigQueryIntro to new Google cloud technologies: Google Storage, Prediction API, BigQuery
Intro to new Google cloud technologies: Google Storage, Prediction API, BigQueryChris Schalk
 
Using Cloud Automation Technologies to Deliver an Enterprise Data Fabric
Using Cloud Automation Technologies to Deliver an Enterprise Data FabricUsing Cloud Automation Technologies to Deliver an Enterprise Data Fabric
Using Cloud Automation Technologies to Deliver an Enterprise Data FabricCambridge Semantics
 
Webinar: Live Data Visualisation with Tableau and MongoDB
Webinar: Live Data Visualisation with Tableau and MongoDBWebinar: Live Data Visualisation with Tableau and MongoDB
Webinar: Live Data Visualisation with Tableau and MongoDBMongoDB
 
PatSeer Premier Overview
PatSeer Premier OverviewPatSeer Premier Overview
PatSeer Premier OverviewGridlogics
 
Secrets of Enterprise Data Mining: SQL Saturday Oregon 201411
Secrets of Enterprise Data Mining: SQL Saturday Oregon 201411Secrets of Enterprise Data Mining: SQL Saturday Oregon 201411
Secrets of Enterprise Data Mining: SQL Saturday Oregon 201411Mark Tabladillo
 
MongoDB Evenings Minneapolis: MongoDB is Cool But When Should I Use It?
MongoDB Evenings Minneapolis: MongoDB is Cool But When Should I Use It?MongoDB Evenings Minneapolis: MongoDB is Cool But When Should I Use It?
MongoDB Evenings Minneapolis: MongoDB is Cool But When Should I Use It?MongoDB
 
PatSeer Projects Overview
PatSeer Projects OverviewPatSeer Projects Overview
PatSeer Projects OverviewGridlogics
 
Linked Data Presentation at TDWI Mpls
Linked Data Presentation at TDWI MplsLinked Data Presentation at TDWI Mpls
Linked Data Presentation at TDWI MplsJay Myers
 
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham AL
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham ALSecrets of Enterprise Data Mining: SQL Saturday 328 Birmingham AL
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham ALMark Tabladillo
 
A Data Model, Workflow, and Architecture for Integrating Data
A Data Model, Workflow, and Architecture for Integrating DataA Data Model, Workflow, and Architecture for Integrating Data
A Data Model, Workflow, and Architecture for Integrating DataDavid Massart
 

La actualidad más candente (17)

Azure data catalog your data your way eugene polonichko dataconf 21 04 18
Azure data catalog your data your way eugene polonichko dataconf 21 04 18Azure data catalog your data your way eugene polonichko dataconf 21 04 18
Azure data catalog your data your way eugene polonichko dataconf 21 04 18
 
PatSeer Lite Overview
PatSeer Lite OverviewPatSeer Lite Overview
PatSeer Lite Overview
 
Orbit Patent Search
Orbit   Patent SearchOrbit   Patent Search
Orbit Patent Search
 
Intro to new Google cloud technologies: Google Storage, Prediction API, BigQuery
Intro to new Google cloud technologies: Google Storage, Prediction API, BigQueryIntro to new Google cloud technologies: Google Storage, Prediction API, BigQuery
Intro to new Google cloud technologies: Google Storage, Prediction API, BigQuery
 
Using Cloud Automation Technologies to Deliver an Enterprise Data Fabric
Using Cloud Automation Technologies to Deliver an Enterprise Data FabricUsing Cloud Automation Technologies to Deliver an Enterprise Data Fabric
Using Cloud Automation Technologies to Deliver an Enterprise Data Fabric
 
Webinar: Live Data Visualisation with Tableau and MongoDB
Webinar: Live Data Visualisation with Tableau and MongoDBWebinar: Live Data Visualisation with Tableau and MongoDB
Webinar: Live Data Visualisation with Tableau and MongoDB
 
PatSeer Premier Overview
PatSeer Premier OverviewPatSeer Premier Overview
PatSeer Premier Overview
 
Secrets of Enterprise Data Mining: SQL Saturday Oregon 201411
Secrets of Enterprise Data Mining: SQL Saturday Oregon 201411Secrets of Enterprise Data Mining: SQL Saturday Oregon 201411
Secrets of Enterprise Data Mining: SQL Saturday Oregon 201411
 
MongoDB Evenings Minneapolis: MongoDB is Cool But When Should I Use It?
MongoDB Evenings Minneapolis: MongoDB is Cool But When Should I Use It?MongoDB Evenings Minneapolis: MongoDB is Cool But When Should I Use It?
MongoDB Evenings Minneapolis: MongoDB is Cool But When Should I Use It?
 
Automated Document Indexing with ImageRamp
Automated Document Indexing with ImageRampAutomated Document Indexing with ImageRamp
Automated Document Indexing with ImageRamp
 
PatSeer Projects Overview
PatSeer Projects OverviewPatSeer Projects Overview
PatSeer Projects Overview
 
Intelligent Data Extraction, Turning Content into Data, A Look at Advanced Ca...
Intelligent Data Extraction, Turning Content into Data, A Look at Advanced Ca...Intelligent Data Extraction, Turning Content into Data, A Look at Advanced Ca...
Intelligent Data Extraction, Turning Content into Data, A Look at Advanced Ca...
 
Automated Data Capture and Extraction with ChronoScan for Automated Metadata ...
Automated Data Capture and Extraction with ChronoScan for Automated Metadata ...Automated Data Capture and Extraction with ChronoScan for Automated Metadata ...
Automated Data Capture and Extraction with ChronoScan for Automated Metadata ...
 
Big data hadoop
Big data hadoopBig data hadoop
Big data hadoop
 
Linked Data Presentation at TDWI Mpls
Linked Data Presentation at TDWI MplsLinked Data Presentation at TDWI Mpls
Linked Data Presentation at TDWI Mpls
 
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham AL
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham ALSecrets of Enterprise Data Mining: SQL Saturday 328 Birmingham AL
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham AL
 
A Data Model, Workflow, and Architecture for Integrating Data
A Data Model, Workflow, and Architecture for Integrating DataA Data Model, Workflow, and Architecture for Integrating Data
A Data Model, Workflow, and Architecture for Integrating Data
 

Similar a Eternus Solutions : Implementation of Salesforce Big Objects

Big Objects in Salesforce
Big Objects in SalesforceBig Objects in Salesforce
Big Objects in SalesforceAmit Chaudhary
 
Big objects in Salesforce Technology
Big objects in Salesforce TechnologyBig objects in Salesforce Technology
Big objects in Salesforce TechnologyDivya Agrawal
 
Microsoft Azure Big Data Analytics
Microsoft Azure Big Data AnalyticsMicrosoft Azure Big Data Analytics
Microsoft Azure Big Data AnalyticsMark Kromer
 
What's new in SQL Server 2016
What's new in SQL Server 2016What's new in SQL Server 2016
What's new in SQL Server 2016James Serra
 
Webinar: How Banks Use MongoDB as a Tick Database
Webinar: How Banks Use MongoDB as a Tick DatabaseWebinar: How Banks Use MongoDB as a Tick Database
Webinar: How Banks Use MongoDB as a Tick DatabaseMongoDB
 
AWS re:Invent 2016: How Amazon S3 Storage Management Helps Optimize Storage a...
AWS re:Invent 2016: How Amazon S3 Storage Management Helps Optimize Storage a...AWS re:Invent 2016: How Amazon S3 Storage Management Helps Optimize Storage a...
AWS re:Invent 2016: How Amazon S3 Storage Management Helps Optimize Storage a...Amazon Web Services
 
Making sense of your data jug
Making sense of your data   jugMaking sense of your data   jug
Making sense of your data jugGerald Muecke
 
Azure Stream Analytics : Analyse Data in Motion
Azure Stream Analytics  : Analyse Data in MotionAzure Stream Analytics  : Analyse Data in Motion
Azure Stream Analytics : Analyse Data in MotionRuhani Arora
 
Realtime Analytics on AWS
Realtime Analytics on AWSRealtime Analytics on AWS
Realtime Analytics on AWSSungmin Kim
 
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...Why Your Data Science Architecture Should Include a Data Virtualization Tool ...
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...Denodo
 
What_to_expect_from_oracle_database_12c
What_to_expect_from_oracle_database_12cWhat_to_expect_from_oracle_database_12c
What_to_expect_from_oracle_database_12cMaria Colgan
 
Big Query - Women Techmarkers (Ukraine - March 2014)
Big Query - Women Techmarkers (Ukraine - March 2014)Big Query - Women Techmarkers (Ukraine - March 2014)
Big Query - Women Techmarkers (Ukraine - March 2014)Ido Green
 
Data Science Machine Lerning Bigdat.pptx
Data Science Machine Lerning Bigdat.pptxData Science Machine Lerning Bigdat.pptx
Data Science Machine Lerning Bigdat.pptxPriyadarshini648418
 
introduction to datawarehouse
introduction to datawarehouseintroduction to datawarehouse
introduction to datawarehousekiran14360
 
Apache IOTDB: a Time Series Database for Industrial IoT
Apache IOTDB: a Time Series Database for Industrial IoTApache IOTDB: a Time Series Database for Industrial IoT
Apache IOTDB: a Time Series Database for Industrial IoTjixuan1989
 
Big Data Expo 2015 - Gigaspaces Making Sense of it all
Big Data Expo 2015 - Gigaspaces Making Sense of it allBig Data Expo 2015 - Gigaspaces Making Sense of it all
Big Data Expo 2015 - Gigaspaces Making Sense of it allBigDataExpo
 
Off-Label Data Mesh: A Prescription for Healthier Data
Off-Label Data Mesh: A Prescription for Healthier DataOff-Label Data Mesh: A Prescription for Healthier Data
Off-Label Data Mesh: A Prescription for Healthier DataHostedbyConfluent
 
Anzo Smart Data Lake 4.0 - a Data Lake Platform for the Enterprise Informatio...
Anzo Smart Data Lake 4.0 - a Data Lake Platform for the Enterprise Informatio...Anzo Smart Data Lake 4.0 - a Data Lake Platform for the Enterprise Informatio...
Anzo Smart Data Lake 4.0 - a Data Lake Platform for the Enterprise Informatio...Cambridge Semantics
 

Similar a Eternus Solutions : Implementation of Salesforce Big Objects (20)

Big Objects in Salesforce
Big Objects in SalesforceBig Objects in Salesforce
Big Objects in Salesforce
 
Big objects in Salesforce Technology
Big objects in Salesforce TechnologyBig objects in Salesforce Technology
Big objects in Salesforce Technology
 
Microsoft Azure Big Data Analytics
Microsoft Azure Big Data AnalyticsMicrosoft Azure Big Data Analytics
Microsoft Azure Big Data Analytics
 
What's new in SQL Server 2016
What's new in SQL Server 2016What's new in SQL Server 2016
What's new in SQL Server 2016
 
Webinar: How Banks Use MongoDB as a Tick Database
Webinar: How Banks Use MongoDB as a Tick DatabaseWebinar: How Banks Use MongoDB as a Tick Database
Webinar: How Banks Use MongoDB as a Tick Database
 
AWS re:Invent 2016: How Amazon S3 Storage Management Helps Optimize Storage a...
AWS re:Invent 2016: How Amazon S3 Storage Management Helps Optimize Storage a...AWS re:Invent 2016: How Amazon S3 Storage Management Helps Optimize Storage a...
AWS re:Invent 2016: How Amazon S3 Storage Management Helps Optimize Storage a...
 
Making sense of your data jug
Making sense of your data   jugMaking sense of your data   jug
Making sense of your data jug
 
Azure Stream Analytics : Analyse Data in Motion
Azure Stream Analytics  : Analyse Data in MotionAzure Stream Analytics  : Analyse Data in Motion
Azure Stream Analytics : Analyse Data in Motion
 
Realtime Analytics on AWS
Realtime Analytics on AWSRealtime Analytics on AWS
Realtime Analytics on AWS
 
MongodB Internals
MongodB InternalsMongodB Internals
MongodB Internals
 
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...Why Your Data Science Architecture Should Include a Data Virtualization Tool ...
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...
 
What_to_expect_from_oracle_database_12c
What_to_expect_from_oracle_database_12cWhat_to_expect_from_oracle_database_12c
What_to_expect_from_oracle_database_12c
 
Big Query - Women Techmarkers (Ukraine - March 2014)
Big Query - Women Techmarkers (Ukraine - March 2014)Big Query - Women Techmarkers (Ukraine - March 2014)
Big Query - Women Techmarkers (Ukraine - March 2014)
 
Distributed Interactive Computing Environment (DICE)
Distributed Interactive Computing Environment (DICE)Distributed Interactive Computing Environment (DICE)
Distributed Interactive Computing Environment (DICE)
 
Data Science Machine Lerning Bigdat.pptx
Data Science Machine Lerning Bigdat.pptxData Science Machine Lerning Bigdat.pptx
Data Science Machine Lerning Bigdat.pptx
 
introduction to datawarehouse
introduction to datawarehouseintroduction to datawarehouse
introduction to datawarehouse
 
Apache IOTDB: a Time Series Database for Industrial IoT
Apache IOTDB: a Time Series Database for Industrial IoTApache IOTDB: a Time Series Database for Industrial IoT
Apache IOTDB: a Time Series Database for Industrial IoT
 
Big Data Expo 2015 - Gigaspaces Making Sense of it all
Big Data Expo 2015 - Gigaspaces Making Sense of it allBig Data Expo 2015 - Gigaspaces Making Sense of it all
Big Data Expo 2015 - Gigaspaces Making Sense of it all
 
Off-Label Data Mesh: A Prescription for Healthier Data
Off-Label Data Mesh: A Prescription for Healthier DataOff-Label Data Mesh: A Prescription for Healthier Data
Off-Label Data Mesh: A Prescription for Healthier Data
 
Anzo Smart Data Lake 4.0 - a Data Lake Platform for the Enterprise Informatio...
Anzo Smart Data Lake 4.0 - a Data Lake Platform for the Enterprise Informatio...Anzo Smart Data Lake 4.0 - a Data Lake Platform for the Enterprise Informatio...
Anzo Smart Data Lake 4.0 - a Data Lake Platform for the Enterprise Informatio...
 

Más de Eternus Solutions

ESPL implementing scalable salesforce integrations for enterprises v1.3
ESPL implementing scalable salesforce integrations for enterprises v1.3ESPL implementing scalable salesforce integrations for enterprises v1.3
ESPL implementing scalable salesforce integrations for enterprises v1.3Eternus Solutions
 
Eternus Solutions : Salesforce Marketing Cloud
Eternus Solutions : Salesforce Marketing Cloud Eternus Solutions : Salesforce Marketing Cloud
Eternus Solutions : Salesforce Marketing Cloud Eternus Solutions
 
Building a Data Quality Inspector with Clicks not Code
Building a Data Quality Inspector with Clicks not CodeBuilding a Data Quality Inspector with Clicks not Code
Building a Data Quality Inspector with Clicks not CodeEternus Solutions
 
Top 18 salesforce winter 18 release feaures with Eternus Solutions
Top 18 salesforce winter 18 release feaures with Eternus SolutionsTop 18 salesforce winter 18 release feaures with Eternus Solutions
Top 18 salesforce winter 18 release feaures with Eternus SolutionsEternus Solutions
 
DREAMFORCE with ETERNUS SOLUTIONS
DREAMFORCE with ETERNUS SOLUTIONSDREAMFORCE with ETERNUS SOLUTIONS
DREAMFORCE with ETERNUS SOLUTIONSEternus Solutions
 

Más de Eternus Solutions (6)

ESPL implementing scalable salesforce integrations for enterprises v1.3
ESPL implementing scalable salesforce integrations for enterprises v1.3ESPL implementing scalable salesforce integrations for enterprises v1.3
ESPL implementing scalable salesforce integrations for enterprises v1.3
 
Eternus Solutions : Salesforce Marketing Cloud
Eternus Solutions : Salesforce Marketing Cloud Eternus Solutions : Salesforce Marketing Cloud
Eternus Solutions : Salesforce Marketing Cloud
 
Salesforce CPQ by Eternus
Salesforce CPQ by EternusSalesforce CPQ by Eternus
Salesforce CPQ by Eternus
 
Building a Data Quality Inspector with Clicks not Code
Building a Data Quality Inspector with Clicks not CodeBuilding a Data Quality Inspector with Clicks not Code
Building a Data Quality Inspector with Clicks not Code
 
Top 18 salesforce winter 18 release feaures with Eternus Solutions
Top 18 salesforce winter 18 release feaures with Eternus SolutionsTop 18 salesforce winter 18 release feaures with Eternus Solutions
Top 18 salesforce winter 18 release feaures with Eternus Solutions
 
DREAMFORCE with ETERNUS SOLUTIONS
DREAMFORCE with ETERNUS SOLUTIONSDREAMFORCE with ETERNUS SOLUTIONS
DREAMFORCE with ETERNUS SOLUTIONS
 

Último

My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024The Digital Insurer
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Enterprise Knowledge
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024Lorenzo Miniero
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Manik S Magar
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfRankYa
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Mark Simos
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embeddingZilliz
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):comworks
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticscarlostorres15106
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationSafe Software
 
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesVector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesZilliz
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxhariprasad279825
 
Vertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsVertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsMiki Katsuragi
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyAlfredo García Lavilla
 

Último (20)

My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdf
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embedding
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
 
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesVector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector Databases
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptx
 
Vertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsVertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering Tips
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easy
 

Eternus Solutions : Implementation of Salesforce Big Objects

  • 1. Implementing Salesforce Big Objects Jigar Shah, Eternus Solutions, Enterprise Architect @jigarshah189 /in/jigarshah189
  • 2. Agenda 2 Need for Big Objects What is a Big Object? Big Object Use Cases Considerations for Usage Demo Q & A
  • 3. Need for Big Objects? 3 Nature of Storage Performance Cost • Master Data • Business Data • Operational Data • Performance diminishes with large data sets • Data retrieval limits • Limited Data Storage
  • 4. What is a Big Object? 4 Object that stores & manages massive data volumes within Salesforce without affecting performance. ▶ Does NOT count against org data storage limits ▶ Processing scale of 1 billion records ▶ Types  Standard (FieldHistoryArchive)  User Defined  Suffixed with “__b”
  • 5. Big Object Use Cases 5 CAPTURE USER ACTIVITY Code reviews, time entries, page views, field audits etc. RETAIN HISTORICAL DATA Historical data stored for compliance 360 CUSTOMER VIEW Ancillary customer data e.g. Purchase Details, Transactions
  • 6. Considerations for Big Objects Usage 6 General UI/ UX Data Security & Access Analytics Packaging • Metadata API • Max. 100 Big Objects per org • Supports DateTime, Lookup, Number, Text, Long Text Area field types only • Triggers, Flows, Processes, Salesforce App are unavailable • Async SOQL is restricted to specific licenses • Standard UI unavailable (Tabs, Detail Pages, List Views) • Works with Visualforce Pages or Lightning Components • Supports Object & Field Permissions only • Included in Managed Packages • No support for Report Builder • Einstein Analytics supported
  • 7. Demo 7 • Use Case • Big Objects Schema Definition • Big Object Record Creation • Data Retrieval • Standard SOQL • Async Soql
  • 8. Demo – Use Case 8 • Extreme Gaming is globally renowned provider of online arcade games. They have an extremely popular game which has thousands of online players. • This company intends to store all the interactions the players make in a single play of the game within Salesforce. • The game has numerous interactions per day which multiplied with its huge set of players results in tons of data.
  • 9. Object Definition 9 Customer Interactions (Customer_Interaction__b) # Field Label Field Name Required? Type Indexed Order 1 In-Game Purchase In_Game_Purchase__c Text (16) 2 Level Achieved Level_Achieved__c Text (16) 3 Lives Used Lives_This_Game__c Text (16) 4 Game Platform Game_Platform__c Yes Text (16) ASC 2 5 Score This Game Score_This_Game__c Text(16) 6 User Account Account__c Yes Lookup (Account) DESC 1 7 Date of Play Play_Date__c Yes DateTime DESC 3 8 Play_Duration__c Play_Duration__c Yes Number (18, 2)
  • 10. Deploying your Schema 10 SchemaDefinition Package.xml Metadata Type Object File Object Definition (Name, Label, Fields) Indexes Permissions File Profile or Permission Set Access
  • 11. Big Object Data Manipulation 11 • Apex CRUD • Create / Update (Idempotent Behavior) • insertImmediate(sobject) OR insertImmediate(sobjects) • Read • SOQL Queries • Async SOQL • CSV Files • API (Bulk API, SOAP API)
  • 12. Using Standard SOQL with Big Objects 12 Executes synchronously All Indexes are mandatory Comparison Operators (=, <, >, <=, >=, IN) Not Supported Operators (!=, LIKE, NOT IN, EXCLUDES, INCLUDES)
  • 13. Using Async SOQL with Big Objects 13 { "jobId":"08PD000000003kiT", "message":"", "query":"SELECT Account__c, In_Game_Purchase__c FROM Customer_Interaction__b WHERE Play_Date__c='2018- 01-05'", "status":"New", "targetObject":"Customer_Interaction_Analysis__c", "targetFieldMap":{ "Account__c":"Account__c", "In_Game_Purchase__c":"Purchase__c" }, "targetValueMap":{ "$JOB_ID":"BackgroundOperationLookup__c", "Copy fields from source to target":"BackgroundOperationDescription__c" } }
  • 14. SOQL Vs Async SOQL Usage Considerations 14 Feature Standard SOQL Async SOQL Mode of Execution Synchronous Asynchronous Immediate Response Required? Yes No Expected Result Set Size Smaller Data Sets (Thousands of records) Large Data Sets (Millions of records) Best Suited For • Displaying Data on UI • Manipulations within Apex • Aggregation • Summarizing for Analytics Filter using Non Index fields Yes No Sample Format SELECT Game_Platform__c, Play_Date__c FROM Customer_Interaction__b WHERE Game_Platform__c='PC' AND Play_Date__c='2017-09-06' { "query": "SELECT Account_c, In_Game_Purchase__c FROM Customer_Interaction__b WHERE Play_Date__c='2017-09-06'", "operation": "insert", "targetObject": "Customer_Interaction_Analysis__c", "targetFieldMap": { "Account__c":"Account__c", "In_Game_Purchase__c":"Purchase__c" }, "targetValueMap":{ "$JOB_ID“ : "BackgroundOperationLookup__c", "Copy fields from source to target“ : "BackgroundOperationDescription__c"} }
  • 15. Additional References 15  Big Object Basics (Trailhead Module)  Big Objects – Bring Data to Force.com (YouTube) Big Objects Implementation Guide (Salesforce Documentation)

Notas del editor

  1. Done