SlideShare a Scribd company logo
1 of 14
Page 1 
Operationalizing value of 
MongoDB 
(MetLife experience) 
Thrills and challenges of building MongoDB 
operations in a large enterprise
Page 2 
A Journey 
When new technology meets enterprise standards : 
- advantages and restrictions of large enterprises 
- it is always a journey 
- decisions we have to live with
Page 3 
Highly Successful Adoption of New Technology 
for a Fortune 50 Enterprise Organization 
• Unknown technology 
– Proves to be capable 
• New platform 
– Quickly matures 
• Untested for the Enterprise 
– Delivers success 
• Many new things to learn 
– Become experts in time
Page 4 
Disclaimer 
• The content in this presentation represents MetLife's choices and MongoDB 
Inc.’s recommendations for MetLife’s specific use case. By no means is this a 
“universal blueprint for success” and it doesn’t necessarily represent 
MongoDB Inc.'s recommendations for all use cases. 
• In particular- because there were some fixed decisions that predated the 
MongoDB implementation, MetLife's deployment may require some 
“manual intervention” (specifically in case of DR) whereas other, differently-organized 
deployments might not.
Page 5 
Introducing “The Wall”
Page 6 
Basic System Architecture Decisions 
• Company Data Center vs. Public Cloud Placement 
Control vs. ease of use 
MetLife: Compliance requirements dictate company data center(s) placement. 
• Server type and sizes 
Enterprise class servers vs. Pizza boxes 
MetLife: More cost effective to run on enterprise class servers - 2x8 Core CPU, 512 GB RAM 
• Virtualization 
VM vs. “Bare Metal” 
MetLife: Data nodes – physical servers, Configuration Servers and MongoS – VMs. 
• SAN vs. Local storage 
Flexibility of SAN vs. performance of local storage 
MetLife: Local storage enclosures. 600 GB SAS drives. 
• Network 
Dedicated LAN for MongoDB replication 
MetLife: No dedicated LANs, for MongoDB installation.
Page 7 
Business Requirements and System Topology 
Business requirements: 
- mission critical application 
- loss of entire data center for indefinite time should not limit the application functionality in 
any way 
- significant data growth is expected, as well as a significant increase in the number of users 
Drive system topology : 
a. Geographic placement 
MetLife: Geographically dispersed cluster, spanning two data centers 
b. Sharded cluster vs. Replica set 
MetLife: Sharded cluster for elastic horizontal scalability 
c. Number of nodes in the replica set 
MetLife: Minimum of 6 to ensure full operability in case of one data center loss. 
d. Writes and reads geography 
MetLife: Business function driven write-concern implementation, reads are mostly 
“secondary preferred”
Page 8 
System topology 
Configuration 
C 
Server 1 
Data Center 1 Data Center 2 
Local Prod 
Replica 1 
Primary 
Prod 
Local Prod 
Hidden Replica 
for 
backups 
Remote Prod 
Replica 1 
Remote Prod 
Replica 2 
Remote Prod 
Hidden Replica 
for 
backups 
Configuration 
Server 2 
Configuration 
Server 3 
Backup 
Solution 
Backup 
Solution 
2 SHARDS 
comprise 
this 
2 SHARDS 
comprise 
this 
2 SHARDS 
comprise 
this 
2 SHARDS 
comprise 
this 
2 SHARDS 
comprise 
this 
2 SHARDS 
comprise 
this 
MongoS Prod 
Server 
MongoS Prod 
Server 
Mongos 
Server 
Mongos Server
Page 9 
System Setup for Availability and DR 
System has to comply with MetLife’s enterprise standard for availability and DR 
(No single points of failure): 
a. Replica sets 
MetLife: 6 member replica sets ( 3 in each data center), 2 hidden replicas for backup purposes, 5 
voting members ( hidden replicas in DR data center has 0 votes), and 2 replicas in primary datacenter 
who have higher priority. 
b. Mongo Configuration servers 
MetLife: 3 configuration servers (2 in primary data center and 1 in DR data center). Loss of entire data 
center halts cluster balancing ability, but not the application functionality. 
c. MongoS 
MetLife: 4 MongoS servers (2 in each data center). All active. 
d. Application servers connectivity 
MetLife: MongoDB drivers on application servers are configured to use all MongoS but in a different 
order for pseudo load balancing. 
e. DR exercise 
MetLife: DR exercise is conducted yearly and includes all database and application infrastructure to 
ensure complete operability from DR data center.
Page 10 
System Set up for Recoverability 
System has to comply with MetLife’s enterprise standard for recoverability: 
Backup and Recovery strategy. 
MetLife: 
- Daily backups in both data centers (alternating). 
- Backups of hidden replicas are performed with mongod brought down. Balancer is 
stopped. 
- Due to the database size backup is performed at the file system level. 
- At the same time backup of Configuration server is performed using mongodump. 
Current challenges. 
MetLife: 
- No point-in-time recovery 
- No easy way to restore one specific database 
Using MMS Backup solution. 
MetLife: 
- MMS Backup is capable of solving some of our current challenges. 
- Due to compliance reasons, cannot use MMS cloud backup solution in AWS 
- Currently looking into an option of running MMS Backup solution on premises
Page 11 
Security 
System has to comply with MetLife’s enterprise standard for data security: 
Authentication and authorization. 
MetLife: 
- Original build in MongoDB 2.2 had very limited options in database authentication 
and write or read/write permission at the database level. 
- Biggest concerns : authentication – no password policy enforcement, authorization – 
excessive application permissions. 
- MetLife’s MongoDB 2.6 goals are : authentication – Active Directory, authorization – 
custom build roles with least set of permission required by application. 
LDAP integration 
MetLife: 
- Integration with Active Directory (AD) using LINUX PAMs 
- Third party product for secure Sever/AD communications 
- Currently mixed mode (both AD and in-database) authentication 
Data-at-rest encryption 
MetLife: Data-at-rest encryption is implemented using third-party product (LINUX file system / 
device encryption). 
Audit. 
MetLife: 
- Tactical: MongoDB 2.6 audit capability can do the job. 
- Strategic: Database activity audit is performed by third party product.
Page 12 
Monitoring and Alerting 
System has to comply with MetLife’s enterprise standard for monitoring and 
alerting: 
Hardware monitoring 
MetLife: No munin-node monitoring. Using standard enterprise Linux server monitoring toolset 
owned by MetLife 
MongoDB monitoring with MMS 
MetLife: Currently using MMS in cloud for monitoring and alerting. Alerts are sent via SMS and 
e-mails to responsible individuals in operations as well as to monitored group mail boxes. 
BMC MongoDB Patrol KM as an alternative monitoring solution 
MetLife: Third party Knowledge Modules are standard monitoring/alerting tools for MetLife’s 
enterprise databases. Currently engaged in MongoDB KM beta-testing. 
Integrating monitoring/alerting to the enterprise incident management system 
MetLife: Currently no integration. Two approaches in parallel: 
- In-house written process to parse JSON attachment from MMS alert e-mail and create 
incident ticket 
- Third party KM is natively integrated with enterprise incident management system
Page 13 
Workload Management and Automation 
System has to reliably support business SLAs and be efficient to manage: 
Workload management and resource sharing. 
MetLife: Workload management and resource sharing is one of the bigger challenges. MongoDB 2.6 
does not have in-database mechanism for managing different workloads, that makes 
resource sharing problematic. 
- Potential options: C-groups in RHEL 6 
MMS automation (installation, upgrades). 
MetLife: Engaged with MongoDB for MMS automation beta-testing.
Page 14 
Next Steps in our Journey 
• Automation (installation, upgrades, maintenance). 
• MMS backup solution (on premises). 
• Monitoring/alerting integration with an incident management system. 
• Workload management / resource sharing solution. 
• Introduction of arbiter to existing replica sets (3rd data center). 
• Performance benchmarking toolset.

More Related Content

What's hot

Google Cloud Platfrom
Google Cloud PlatfromGoogle Cloud Platfrom
Google Cloud PlatfromVirendra Bora
 
大量のデータ処理や分析に使えるOSS Apache Spark入門 - Open Source Conference2020 Online/Fukuoka...
大量のデータ処理や分析に使えるOSS Apache Spark入門 - Open Source Conference2020 Online/Fukuoka...大量のデータ処理や分析に使えるOSS Apache Spark入門 - Open Source Conference2020 Online/Fukuoka...
大量のデータ処理や分析に使えるOSS Apache Spark入門 - Open Source Conference2020 Online/Fukuoka...NTT DATA Technology & Innovation
 
[Cloud OnAir] Google Cloud へのデータ移行 2019年1月24日 放送
[Cloud OnAir] Google Cloud へのデータ移行 2019年1月24日 放送[Cloud OnAir] Google Cloud へのデータ移行 2019年1月24日 放送
[Cloud OnAir] Google Cloud へのデータ移行 2019年1月24日 放送Google Cloud Platform - Japan
 
OCI 購入モデルの整理と Universal Credit 最新情報(2021年2月17日版)
OCI 購入モデルの整理と Universal Credit 最新情報(2021年2月17日版)OCI 購入モデルの整理と Universal Credit 最新情報(2021年2月17日版)
OCI 購入モデルの整理と Universal Credit 最新情報(2021年2月17日版)オラクルエンジニア通信
 
Oracle Database: リリースモデルとアップグレード・パッチ計画 (2021年2月版)
Oracle Database: リリースモデルとアップグレード・パッチ計画 (2021年2月版)Oracle Database: リリースモデルとアップグレード・パッチ計画 (2021年2月版)
Oracle Database: リリースモデルとアップグレード・パッチ計画 (2021年2月版)オラクルエンジニア通信
 
GoldenGateテクニカルセミナー3「Oracle GoldenGate Technical Deep Dive」(2016/5/11)
GoldenGateテクニカルセミナー3「Oracle GoldenGate Technical Deep Dive」(2016/5/11)GoldenGateテクニカルセミナー3「Oracle GoldenGate Technical Deep Dive」(2016/5/11)
GoldenGateテクニカルセミナー3「Oracle GoldenGate Technical Deep Dive」(2016/5/11)オラクルエンジニア通信
 
Mongo dbを知ろう
Mongo dbを知ろうMongo dbを知ろう
Mongo dbを知ろうCROOZ, inc.
 
しばちょう先生が語る!オラクルデータベースの進化の歴史と最新技術動向#2
しばちょう先生が語る!オラクルデータベースの進化の歴史と最新技術動向#2しばちょう先生が語る!オラクルデータベースの進化の歴史と最新技術動向#2
しばちょう先生が語る!オラクルデータベースの進化の歴史と最新技術動向#2オラクルエンジニア通信
 
クラウドのコストを大幅削減!事例から見るクラウド間移行の効果(Oracle Cloudウェビナーシリーズ: 2020年7月8日)
クラウドのコストを大幅削減!事例から見るクラウド間移行の効果(Oracle Cloudウェビナーシリーズ: 2020年7月8日)クラウドのコストを大幅削減!事例から見るクラウド間移行の効果(Oracle Cloudウェビナーシリーズ: 2020年7月8日)
クラウドのコストを大幅削減!事例から見るクラウド間移行の効果(Oracle Cloudウェビナーシリーズ: 2020年7月8日)オラクルエンジニア通信
 
Synapse lakedatabase
Synapse lakedatabaseSynapse lakedatabase
Synapse lakedatabaseRyoma Nagata
 
FPGAによる大規模データ処理の高速化
FPGAによる大規模データ処理の高速化FPGAによる大規模データ処理の高速化
FPGAによる大規模データ処理の高速化Kazunori Sato
 
IoT風速計を作った話
IoT風速計を作った話IoT風速計を作った話
IoT風速計を作った話YuitaTakenaka
 
MongoDBのはじめての運用テキスト
MongoDBのはじめての運用テキストMongoDBのはじめての運用テキスト
MongoDBのはじめての運用テキストAkihiro Kuwano
 
Apache Spark on Kubernetes入門(Open Source Conference 2021 Online Hiroshima 発表資料)
Apache Spark on Kubernetes入門(Open Source Conference 2021 Online Hiroshima 発表資料)Apache Spark on Kubernetes入門(Open Source Conference 2021 Online Hiroshima 発表資料)
Apache Spark on Kubernetes入門(Open Source Conference 2021 Online Hiroshima 発表資料)NTT DATA Technology & Innovation
 
[Cloud OnAir] お客様事例紹介 アサヒグループのデータと GCP の活用 2019年6月13日 放送
[Cloud OnAir] お客様事例紹介  アサヒグループのデータと GCP の活用 2019年6月13日 放送[Cloud OnAir] お客様事例紹介  アサヒグループのデータと GCP の活用 2019年6月13日 放送
[Cloud OnAir] お客様事例紹介 アサヒグループのデータと GCP の活用 2019年6月13日 放送Google Cloud Platform - Japan
 
Spannerをrestでつかってみた
SpannerをrestでつかってみたSpannerをrestでつかってみた
SpannerをrestでつかってみたHayato Ito
 

What's hot (20)

Google Cloud Platfrom
Google Cloud PlatfromGoogle Cloud Platfrom
Google Cloud Platfrom
 
大量のデータ処理や分析に使えるOSS Apache Spark入門 - Open Source Conference2020 Online/Fukuoka...
大量のデータ処理や分析に使えるOSS Apache Spark入門 - Open Source Conference2020 Online/Fukuoka...大量のデータ処理や分析に使えるOSS Apache Spark入門 - Open Source Conference2020 Online/Fukuoka...
大量のデータ処理や分析に使えるOSS Apache Spark入門 - Open Source Conference2020 Online/Fukuoka...
 
[Cloud OnAir] Google Cloud へのデータ移行 2019年1月24日 放送
[Cloud OnAir] Google Cloud へのデータ移行 2019年1月24日 放送[Cloud OnAir] Google Cloud へのデータ移行 2019年1月24日 放送
[Cloud OnAir] Google Cloud へのデータ移行 2019年1月24日 放送
 
OCI 購入モデルの整理と Universal Credit 最新情報(2021年2月17日版)
OCI 購入モデルの整理と Universal Credit 最新情報(2021年2月17日版)OCI 購入モデルの整理と Universal Credit 最新情報(2021年2月17日版)
OCI 購入モデルの整理と Universal Credit 最新情報(2021年2月17日版)
 
pg_bigmを用いた全文検索のしくみ(前編)
pg_bigmを用いた全文検索のしくみ(前編)pg_bigmを用いた全文検索のしくみ(前編)
pg_bigmを用いた全文検索のしくみ(前編)
 
Oracle Database: リリースモデルとアップグレード・パッチ計画 (2021年2月版)
Oracle Database: リリースモデルとアップグレード・パッチ計画 (2021年2月版)Oracle Database: リリースモデルとアップグレード・パッチ計画 (2021年2月版)
Oracle Database: リリースモデルとアップグレード・パッチ計画 (2021年2月版)
 
GoldenGateテクニカルセミナー3「Oracle GoldenGate Technical Deep Dive」(2016/5/11)
GoldenGateテクニカルセミナー3「Oracle GoldenGate Technical Deep Dive」(2016/5/11)GoldenGateテクニカルセミナー3「Oracle GoldenGate Technical Deep Dive」(2016/5/11)
GoldenGateテクニカルセミナー3「Oracle GoldenGate Technical Deep Dive」(2016/5/11)
 
Big data security
Big data securityBig data security
Big data security
 
Mongo dbを知ろう
Mongo dbを知ろうMongo dbを知ろう
Mongo dbを知ろう
 
しばちょう先生が語る!オラクルデータベースの進化の歴史と最新技術動向#2
しばちょう先生が語る!オラクルデータベースの進化の歴史と最新技術動向#2しばちょう先生が語る!オラクルデータベースの進化の歴史と最新技術動向#2
しばちょう先生が語る!オラクルデータベースの進化の歴史と最新技術動向#2
 
クラウドのコストを大幅削減!事例から見るクラウド間移行の効果(Oracle Cloudウェビナーシリーズ: 2020年7月8日)
クラウドのコストを大幅削減!事例から見るクラウド間移行の効果(Oracle Cloudウェビナーシリーズ: 2020年7月8日)クラウドのコストを大幅削減!事例から見るクラウド間移行の効果(Oracle Cloudウェビナーシリーズ: 2020年7月8日)
クラウドのコストを大幅削減!事例から見るクラウド間移行の効果(Oracle Cloudウェビナーシリーズ: 2020年7月8日)
 
Synapse lakedatabase
Synapse lakedatabaseSynapse lakedatabase
Synapse lakedatabase
 
Oracle GoldenGate アーキテクチャと基本機能
Oracle GoldenGate アーキテクチャと基本機能Oracle GoldenGate アーキテクチャと基本機能
Oracle GoldenGate アーキテクチャと基本機能
 
第18回しゃちほこオラクル俱楽部
第18回しゃちほこオラクル俱楽部第18回しゃちほこオラクル俱楽部
第18回しゃちほこオラクル俱楽部
 
FPGAによる大規模データ処理の高速化
FPGAによる大規模データ処理の高速化FPGAによる大規模データ処理の高速化
FPGAによる大規模データ処理の高速化
 
IoT風速計を作った話
IoT風速計を作った話IoT風速計を作った話
IoT風速計を作った話
 
MongoDBのはじめての運用テキスト
MongoDBのはじめての運用テキストMongoDBのはじめての運用テキスト
MongoDBのはじめての運用テキスト
 
Apache Spark on Kubernetes入門(Open Source Conference 2021 Online Hiroshima 発表資料)
Apache Spark on Kubernetes入門(Open Source Conference 2021 Online Hiroshima 発表資料)Apache Spark on Kubernetes入門(Open Source Conference 2021 Online Hiroshima 発表資料)
Apache Spark on Kubernetes入門(Open Source Conference 2021 Online Hiroshima 発表資料)
 
[Cloud OnAir] お客様事例紹介 アサヒグループのデータと GCP の活用 2019年6月13日 放送
[Cloud OnAir] お客様事例紹介  アサヒグループのデータと GCP の活用 2019年6月13日 放送[Cloud OnAir] お客様事例紹介  アサヒグループのデータと GCP の活用 2019年6月13日 放送
[Cloud OnAir] お客様事例紹介 アサヒグループのデータと GCP の活用 2019年6月13日 放送
 
Spannerをrestでつかってみた
SpannerをrestでつかってみたSpannerをrestでつかってみた
Spannerをrestでつかってみた
 

Similar to Operationalizing the Value of MongoDB: The MetLife Experience

Computing And Information Technology Programmes Essay
Computing And Information Technology Programmes EssayComputing And Information Technology Programmes Essay
Computing And Information Technology Programmes EssayLucy Nader
 
Webinar: Enterprise Trends for Database-as-a-Service
Webinar: Enterprise Trends for Database-as-a-ServiceWebinar: Enterprise Trends for Database-as-a-Service
Webinar: Enterprise Trends for Database-as-a-ServiceMongoDB
 
Cognos Data Module Architectures & Use Cases
Cognos Data Module Architectures & Use CasesCognos Data Module Architectures & Use Cases
Cognos Data Module Architectures & Use CasesSenturus
 
Introducing MongoDB 2.6
Introducing MongoDB 2.6Introducing MongoDB 2.6
Introducing MongoDB 2.6MongoDB
 
Webinar: Enterprise Trends for Database-as-a-Service
Webinar: Enterprise Trends for Database-as-a-ServiceWebinar: Enterprise Trends for Database-as-a-Service
Webinar: Enterprise Trends for Database-as-a-ServiceMongoDB
 
How to boost performance of your rails app using dynamo db and memcached
How to boost performance of your rails app using dynamo db and memcachedHow to boost performance of your rails app using dynamo db and memcached
How to boost performance of your rails app using dynamo db and memcachedAndolasoft Inc
 
MongoDB on Financial Services Sector
MongoDB on Financial Services SectorMongoDB on Financial Services Sector
MongoDB on Financial Services SectorNorberto Leite
 
Performance tuning datasheet
Performance tuning datasheetPerformance tuning datasheet
Performance tuning datasheetGlobalSoftUSA
 
Membase Meetup 2010
Membase Meetup 2010Membase Meetup 2010
Membase Meetup 2010Membase
 
Webinar: High Performance MongoDB Applications with IBM POWER8
Webinar: High Performance MongoDB Applications with IBM POWER8Webinar: High Performance MongoDB Applications with IBM POWER8
Webinar: High Performance MongoDB Applications with IBM POWER8MongoDB
 
La creación de una capa operacional con MongoDB
La creación de una capa operacional con MongoDBLa creación de una capa operacional con MongoDB
La creación de una capa operacional con MongoDBMongoDB
 
Scaling Databricks to Run Data and ML Workloads on Millions of VMs
Scaling Databricks to Run Data and ML Workloads on Millions of VMsScaling Databricks to Run Data and ML Workloads on Millions of VMs
Scaling Databricks to Run Data and ML Workloads on Millions of VMsMatei Zaharia
 
how_can_businesses_address_storage_issues_using_mongodb.pptx
how_can_businesses_address_storage_issues_using_mongodb.pptxhow_can_businesses_address_storage_issues_using_mongodb.pptx
how_can_businesses_address_storage_issues_using_mongodb.pptxsarah david
 
Lessons from Large-Scale Cloud Software at Databricks
Lessons from Large-Scale Cloud Software at DatabricksLessons from Large-Scale Cloud Software at Databricks
Lessons from Large-Scale Cloud Software at DatabricksMatei Zaharia
 
Webinar: What's New in MongoDB 3.2
Webinar: What's New in MongoDB 3.2Webinar: What's New in MongoDB 3.2
Webinar: What's New in MongoDB 3.2MongoDB
 

Similar to Operationalizing the Value of MongoDB: The MetLife Experience (20)

Mdb dn 2016_12_single_view
Mdb dn 2016_12_single_viewMdb dn 2016_12_single_view
Mdb dn 2016_12_single_view
 
Computing And Information Technology Programmes Essay
Computing And Information Technology Programmes EssayComputing And Information Technology Programmes Essay
Computing And Information Technology Programmes Essay
 
Webinar: Enterprise Trends for Database-as-a-Service
Webinar: Enterprise Trends for Database-as-a-ServiceWebinar: Enterprise Trends for Database-as-a-Service
Webinar: Enterprise Trends for Database-as-a-Service
 
Cognos Data Module Architectures & Use Cases
Cognos Data Module Architectures & Use CasesCognos Data Module Architectures & Use Cases
Cognos Data Module Architectures & Use Cases
 
Introducing MongoDB 2.6
Introducing MongoDB 2.6Introducing MongoDB 2.6
Introducing MongoDB 2.6
 
Ameya_Kasbekar_Resume
Ameya_Kasbekar_ResumeAmeya_Kasbekar_Resume
Ameya_Kasbekar_Resume
 
AtomicDBCoreTech_White Papaer
AtomicDBCoreTech_White PapaerAtomicDBCoreTech_White Papaer
AtomicDBCoreTech_White Papaer
 
Clustering overview2
Clustering overview2Clustering overview2
Clustering overview2
 
Webinar: Enterprise Trends for Database-as-a-Service
Webinar: Enterprise Trends for Database-as-a-ServiceWebinar: Enterprise Trends for Database-as-a-Service
Webinar: Enterprise Trends for Database-as-a-Service
 
How to boost performance of your rails app using dynamo db and memcached
How to boost performance of your rails app using dynamo db and memcachedHow to boost performance of your rails app using dynamo db and memcached
How to boost performance of your rails app using dynamo db and memcached
 
MongoDB on Financial Services Sector
MongoDB on Financial Services SectorMongoDB on Financial Services Sector
MongoDB on Financial Services Sector
 
Performance tuning datasheet
Performance tuning datasheetPerformance tuning datasheet
Performance tuning datasheet
 
Membase Meetup 2010
Membase Meetup 2010Membase Meetup 2010
Membase Meetup 2010
 
Webinar: High Performance MongoDB Applications with IBM POWER8
Webinar: High Performance MongoDB Applications with IBM POWER8Webinar: High Performance MongoDB Applications with IBM POWER8
Webinar: High Performance MongoDB Applications with IBM POWER8
 
La creación de una capa operacional con MongoDB
La creación de una capa operacional con MongoDBLa creación de una capa operacional con MongoDB
La creación de una capa operacional con MongoDB
 
BigData Analysis
BigData AnalysisBigData Analysis
BigData Analysis
 
Scaling Databricks to Run Data and ML Workloads on Millions of VMs
Scaling Databricks to Run Data and ML Workloads on Millions of VMsScaling Databricks to Run Data and ML Workloads on Millions of VMs
Scaling Databricks to Run Data and ML Workloads on Millions of VMs
 
how_can_businesses_address_storage_issues_using_mongodb.pptx
how_can_businesses_address_storage_issues_using_mongodb.pptxhow_can_businesses_address_storage_issues_using_mongodb.pptx
how_can_businesses_address_storage_issues_using_mongodb.pptx
 
Lessons from Large-Scale Cloud Software at Databricks
Lessons from Large-Scale Cloud Software at DatabricksLessons from Large-Scale Cloud Software at Databricks
Lessons from Large-Scale Cloud Software at Databricks
 
Webinar: What's New in MongoDB 3.2
Webinar: What's New in MongoDB 3.2Webinar: What's New in MongoDB 3.2
Webinar: What's New in MongoDB 3.2
 

More from MongoDB

MongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
 
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB
 
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB
 
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB
 
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB
 
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB
 
MongoDB SoCal 2020: MongoDB Atlas Jump Start
 MongoDB SoCal 2020: MongoDB Atlas Jump Start MongoDB SoCal 2020: MongoDB Atlas Jump Start
MongoDB SoCal 2020: MongoDB Atlas Jump StartMongoDB
 
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB
 
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB
 
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB
 
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB
 
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB
 
MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB
 
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB
 
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB
 
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB
 
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB
 
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB
 
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB
 
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB
 

More from MongoDB (20)

MongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
 
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
 
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
 
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
 
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
 
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
 
MongoDB SoCal 2020: MongoDB Atlas Jump Start
 MongoDB SoCal 2020: MongoDB Atlas Jump Start MongoDB SoCal 2020: MongoDB Atlas Jump Start
MongoDB SoCal 2020: MongoDB Atlas Jump Start
 
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
 
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
 
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
 
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
 
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
 
MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
 
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
 
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
 
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
 
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
 
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
 
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
 
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
 

Recently uploaded

Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024The Digital Insurer
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking MenDelhi Call girls
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)wesley chun
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreternaman860154
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024The Digital Insurer
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEarley Information Science
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CVKhem
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfEnterprise Knowledge
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationMichael W. Hawkins
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Igalia
 
A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024Results
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...apidays
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slidespraypatel2
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Scriptwesley chun
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘RTylerCroy
 

Recently uploaded (20)

Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
 
A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slides
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 

Operationalizing the Value of MongoDB: The MetLife Experience

  • 1. Page 1 Operationalizing value of MongoDB (MetLife experience) Thrills and challenges of building MongoDB operations in a large enterprise
  • 2. Page 2 A Journey When new technology meets enterprise standards : - advantages and restrictions of large enterprises - it is always a journey - decisions we have to live with
  • 3. Page 3 Highly Successful Adoption of New Technology for a Fortune 50 Enterprise Organization • Unknown technology – Proves to be capable • New platform – Quickly matures • Untested for the Enterprise – Delivers success • Many new things to learn – Become experts in time
  • 4. Page 4 Disclaimer • The content in this presentation represents MetLife's choices and MongoDB Inc.’s recommendations for MetLife’s specific use case. By no means is this a “universal blueprint for success” and it doesn’t necessarily represent MongoDB Inc.'s recommendations for all use cases. • In particular- because there were some fixed decisions that predated the MongoDB implementation, MetLife's deployment may require some “manual intervention” (specifically in case of DR) whereas other, differently-organized deployments might not.
  • 5. Page 5 Introducing “The Wall”
  • 6. Page 6 Basic System Architecture Decisions • Company Data Center vs. Public Cloud Placement Control vs. ease of use MetLife: Compliance requirements dictate company data center(s) placement. • Server type and sizes Enterprise class servers vs. Pizza boxes MetLife: More cost effective to run on enterprise class servers - 2x8 Core CPU, 512 GB RAM • Virtualization VM vs. “Bare Metal” MetLife: Data nodes – physical servers, Configuration Servers and MongoS – VMs. • SAN vs. Local storage Flexibility of SAN vs. performance of local storage MetLife: Local storage enclosures. 600 GB SAS drives. • Network Dedicated LAN for MongoDB replication MetLife: No dedicated LANs, for MongoDB installation.
  • 7. Page 7 Business Requirements and System Topology Business requirements: - mission critical application - loss of entire data center for indefinite time should not limit the application functionality in any way - significant data growth is expected, as well as a significant increase in the number of users Drive system topology : a. Geographic placement MetLife: Geographically dispersed cluster, spanning two data centers b. Sharded cluster vs. Replica set MetLife: Sharded cluster for elastic horizontal scalability c. Number of nodes in the replica set MetLife: Minimum of 6 to ensure full operability in case of one data center loss. d. Writes and reads geography MetLife: Business function driven write-concern implementation, reads are mostly “secondary preferred”
  • 8. Page 8 System topology Configuration C Server 1 Data Center 1 Data Center 2 Local Prod Replica 1 Primary Prod Local Prod Hidden Replica for backups Remote Prod Replica 1 Remote Prod Replica 2 Remote Prod Hidden Replica for backups Configuration Server 2 Configuration Server 3 Backup Solution Backup Solution 2 SHARDS comprise this 2 SHARDS comprise this 2 SHARDS comprise this 2 SHARDS comprise this 2 SHARDS comprise this 2 SHARDS comprise this MongoS Prod Server MongoS Prod Server Mongos Server Mongos Server
  • 9. Page 9 System Setup for Availability and DR System has to comply with MetLife’s enterprise standard for availability and DR (No single points of failure): a. Replica sets MetLife: 6 member replica sets ( 3 in each data center), 2 hidden replicas for backup purposes, 5 voting members ( hidden replicas in DR data center has 0 votes), and 2 replicas in primary datacenter who have higher priority. b. Mongo Configuration servers MetLife: 3 configuration servers (2 in primary data center and 1 in DR data center). Loss of entire data center halts cluster balancing ability, but not the application functionality. c. MongoS MetLife: 4 MongoS servers (2 in each data center). All active. d. Application servers connectivity MetLife: MongoDB drivers on application servers are configured to use all MongoS but in a different order for pseudo load balancing. e. DR exercise MetLife: DR exercise is conducted yearly and includes all database and application infrastructure to ensure complete operability from DR data center.
  • 10. Page 10 System Set up for Recoverability System has to comply with MetLife’s enterprise standard for recoverability: Backup and Recovery strategy. MetLife: - Daily backups in both data centers (alternating). - Backups of hidden replicas are performed with mongod brought down. Balancer is stopped. - Due to the database size backup is performed at the file system level. - At the same time backup of Configuration server is performed using mongodump. Current challenges. MetLife: - No point-in-time recovery - No easy way to restore one specific database Using MMS Backup solution. MetLife: - MMS Backup is capable of solving some of our current challenges. - Due to compliance reasons, cannot use MMS cloud backup solution in AWS - Currently looking into an option of running MMS Backup solution on premises
  • 11. Page 11 Security System has to comply with MetLife’s enterprise standard for data security: Authentication and authorization. MetLife: - Original build in MongoDB 2.2 had very limited options in database authentication and write or read/write permission at the database level. - Biggest concerns : authentication – no password policy enforcement, authorization – excessive application permissions. - MetLife’s MongoDB 2.6 goals are : authentication – Active Directory, authorization – custom build roles with least set of permission required by application. LDAP integration MetLife: - Integration with Active Directory (AD) using LINUX PAMs - Third party product for secure Sever/AD communications - Currently mixed mode (both AD and in-database) authentication Data-at-rest encryption MetLife: Data-at-rest encryption is implemented using third-party product (LINUX file system / device encryption). Audit. MetLife: - Tactical: MongoDB 2.6 audit capability can do the job. - Strategic: Database activity audit is performed by third party product.
  • 12. Page 12 Monitoring and Alerting System has to comply with MetLife’s enterprise standard for monitoring and alerting: Hardware monitoring MetLife: No munin-node monitoring. Using standard enterprise Linux server monitoring toolset owned by MetLife MongoDB monitoring with MMS MetLife: Currently using MMS in cloud for monitoring and alerting. Alerts are sent via SMS and e-mails to responsible individuals in operations as well as to monitored group mail boxes. BMC MongoDB Patrol KM as an alternative monitoring solution MetLife: Third party Knowledge Modules are standard monitoring/alerting tools for MetLife’s enterprise databases. Currently engaged in MongoDB KM beta-testing. Integrating monitoring/alerting to the enterprise incident management system MetLife: Currently no integration. Two approaches in parallel: - In-house written process to parse JSON attachment from MMS alert e-mail and create incident ticket - Third party KM is natively integrated with enterprise incident management system
  • 13. Page 13 Workload Management and Automation System has to reliably support business SLAs and be efficient to manage: Workload management and resource sharing. MetLife: Workload management and resource sharing is one of the bigger challenges. MongoDB 2.6 does not have in-database mechanism for managing different workloads, that makes resource sharing problematic. - Potential options: C-groups in RHEL 6 MMS automation (installation, upgrades). MetLife: Engaged with MongoDB for MMS automation beta-testing.
  • 14. Page 14 Next Steps in our Journey • Automation (installation, upgrades, maintenance). • MMS backup solution (on premises). • Monitoring/alerting integration with an incident management system. • Workload management / resource sharing solution. • Introduction of arbiter to existing replica sets (3rd data center). • Performance benchmarking toolset.