1 00:00:00,400 --> 00:00:02,570 ‫So let's summarize everything we know 2 00:00:02,570 --> 00:00:05,390 ‫about databases and analytics in AWS. 3 00:00:05,390 --> 00:00:08,540 ‫And you just need to know what database corresponds 4 00:00:08,540 --> 00:00:10,690 ‫to what use case going into the exam. 5 00:00:10,690 --> 00:00:13,540 ‫So if you have a Relational database, and you have OLTP, 6 00:00:13,540 --> 00:00:15,640 ‫Online Transaction Processing, you should think 7 00:00:15,640 --> 00:00:19,837 ‫RDS and Aurora and both these databases supports the SQL, 8 00:00:20,830 --> 00:00:23,950 ‫language, SQL language to query your data. 9 00:00:23,950 --> 00:00:25,570 ‫You also need to know for RDS the difference 10 00:00:25,570 --> 00:00:28,370 ‫between a Multi-AZ deployment, Read Replicas 11 00:00:28,370 --> 00:00:31,320 ‫and Multi-regions as well as their use cases. 12 00:00:31,320 --> 00:00:33,960 ‫If you need to find an in-memory database 13 00:00:33,960 --> 00:00:36,680 ‫or in-memory cache, think ElastiCache. 14 00:00:36,680 --> 00:00:38,840 ‫If you're looking for a key value database, 15 00:00:38,840 --> 00:00:41,340 ‫thing DynamoDB, which is a serverless database. 16 00:00:41,340 --> 00:00:44,120 ‫And if you need caching technology for DynamoDB, 17 00:00:44,120 --> 00:00:46,760 ‫then use the DAX, technology which is cache 18 00:00:46,760 --> 00:00:49,900 ‫made specifically for DynamoDB. 19 00:00:49,900 --> 00:00:52,810 ‫If you're looking for a word data warehousing, or OLAP, 20 00:00:52,810 --> 00:00:56,450 ‫online analytical processing, then you need to look 21 00:00:56,450 --> 00:00:59,050 ‫at Redshift, which is a warehousing technology. 22 00:00:59,050 --> 00:01:00,950 ‫And you can also use the SQL language 23 00:01:00,950 --> 00:01:04,060 ‫to query data on Redshift, if you're trying to build 24 00:01:04,060 --> 00:01:06,470 ‫a Hadoop Cluster to do big data analysis, 25 00:01:06,470 --> 00:01:10,460 ‫use the EMR service, if you want to query data on Amazon S3 26 00:01:10,460 --> 00:01:12,610 ‫in a serverless fashion with the SQL language, 27 00:01:12,610 --> 00:01:16,580 ‫then use Athena, QuickSight is a way to create dashboards 28 00:01:16,580 --> 00:01:19,960 ‫visually interactives, visuals and so on. 29 00:01:19,960 --> 00:01:21,830 ‫That can be interactive as well on your data 30 00:01:21,830 --> 00:01:23,610 ‫in a serverless fashion, then you will use Amazon 31 00:01:23,610 --> 00:01:26,990 ‫QuickSights also used for business intelligence. 32 00:01:26,990 --> 00:01:30,550 ‫DocumentDB is what I call the Aura of MongoDB. 33 00:01:30,550 --> 00:01:33,370 ‫So anytime you see MongoDB think DocumentDB, 34 00:01:33,370 --> 00:01:36,130 ‫which is also using the JSON type of data sets. 35 00:01:36,130 --> 00:01:38,100 ‫And this is a no SQL database. 36 00:01:38,100 --> 00:01:41,780 ‫So this is another no SQL database on top of DynamoDB. 37 00:01:41,780 --> 00:01:45,110 ‫Then we have Amazon QLDB, which is a financial 38 00:01:45,110 --> 00:01:47,670 ‫transaction ledger, anytime you would see financial 39 00:01:47,670 --> 00:01:49,900 ‫transaction, immutable journal something 40 00:01:49,900 --> 00:01:51,730 ‫that is cryptographically verifiable, 41 00:01:51,730 --> 00:01:53,990 ‫think Amazon QLDB, and this is a central 42 00:01:53,990 --> 00:01:57,480 ‫database which is opposed to a decentralized database, 43 00:01:57,480 --> 00:02:00,030 ‫which is Amazon Managed Blockchain, in which case, 44 00:02:00,030 --> 00:02:02,200 ‫we can have manage Hyperledger Fabric 45 00:02:02,200 --> 00:02:05,120 ‫and Ethereum blockchains on AWS. 46 00:02:05,120 --> 00:02:08,090 ‫Finally, if you want to have a managed extract, 47 00:02:08,090 --> 00:02:11,380 ‫transform and load tools ETL, we can use Glue, 48 00:02:11,380 --> 00:02:14,820 ‫which also has a data catalog service to discover datasets 49 00:02:14,820 --> 00:02:17,700 ‫into your various databases in AWS. 50 00:02:17,700 --> 00:02:20,630 ‫And finally, if you need to move data between databases, 51 00:02:20,630 --> 00:02:22,320 ‫then you would use the DMS service 52 00:02:22,320 --> 00:02:24,380 ‫for database migration service. 53 00:02:24,380 --> 00:02:26,440 ‫Oh, so I don't forget, there is Neptune 54 00:02:26,440 --> 00:02:29,410 ‫if you have a graph database, that's it. 55 00:02:29,410 --> 00:02:32,360 ‫I hope you liked it and I will see you in the next lecture.