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Demystifying Data Warehousing as a Service - DFW
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This is the extended presentation I gave to the DFW Data Science meetup on Feb 5, 2018.
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Demystifying Data Warehousing as a Service - DFW
1.
1Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Y O U R D A T A , N O L I M I T S Ā© 2017 Snowflake Computing Inc. All Rights Reserved. @KentGraziano KENT GRAZIANO Chief Technical Evangelist Snowflake Computing Demystifying Data Warehousing as a Service (DWaaS)
2.
2Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. ā¢ Chief Technical Evangelist, Snowflake Computing ā¢ Oracle ACE Director (DW/BI) ā¢ OakTable Network ā¢ Blogger ā The Data Warrior ā¢ Certified Data Vault Master and DV 2.0 Practitioner ā¢ Former Member: Boulder BI Brain Trust (#BBBT) ā¢ Member: DAMA Houston & DAMA International ā¢ Data Architecture and Data Warehouse Specialist ā¢ 30+ years in IT ā¢ 25+ years of Oracle-related work ā¢ 20+ years of data warehousing experience ā¢ Author & Co-Author of a bunch of books (Amazon) ā¢ Past-President of ODTUG and Rocky Mountain Oracle User Group My Bio
3.
3Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. About Snowflake Experienced, accomplished leadership team 2012 Founded by industry veterans with over 120 database patents Vision: A world with no limits on data First data warehouse built for the cloud Over 1000 customers since GA
4.
4Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. ā¢ Data Challenges ā¢ What is a Data Warehouse as a Service? ā¢ What can a Cloud DWaaS do for me? ā¢ Features of a Cloud DWaaS ā¢ Top 10 (or so) Cool Features of Snowflake ā¢ Becoming more Agile with DWaaS ā¢ Where Snowflake fits ā¢ Spark Integration ā¢ Continuous Loading ā¢ Conclusion Agenda
5.
5Ā© 2017 Snowflake
Computing Inc. All Rights Reserved.Ā© 2017 Snowflake Computing Inc. All Rights Reserved. Data challenges today
6.
6Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Scenarios Ā with Ā affinity Ā for Ā cloud Gartner 2016 Predictions: By 2018, six billion connected things will be requesting support. Connecting applications, devices, and āthingsā Reaching employees, business partners, and consumers Anytime, anywhere mobility On demand, unlimited scale Understanding behavior; generating, retaining, and analyzing data
7.
7Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. 40 Zettabytes by 2020 Web ERP3rd party apps Enterprise apps IoTMobile
8.
8Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Itās not the data itself itās how you take full advantage of the insight it provides Web ERP3rd party apps Enterprise apps IoTMobile
9.
9Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Most firms donāt consistently turn data into action Source: Forrester All Possible data All Possible Action of firms aspire to be data-driven. 73% of firms are good at turning data into action. 29%
10.
10Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. The data struggle Data is stale Hard to incorporate new data sources Too much time waiting for data Too much time spent on manual administration Reports are too slow whenever the system is busy Hard to experiment with data
11.
11Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Symptoms of fundamental challenges Data silos Data locked into separate databases, big data systems, and applications Inflexibility Slow, cumbersome scaling and limited support for diverse data Complexity Multiple systems to integrate and manage requiring specialized skills and tools Performance Contention for limited resources resulting in latency and delays Cost Painful upfront costs and overprovisioned capacity
12.
12Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. The evolution of data platforms Data warehouse & platform software Vertica, Greenplum, Paraccel, Hadoop Data warehouse appliance Teradata 1990s 2000s 2010s Cloud DWaaS Snowflake 1980s Relational database Oracle, DB2, SQL Server
13.
13Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. What is a Cloud DWaaS? DW- Data Warehouse ā¢ Relational database ā¢ Uses standard SQL ā¢ Optimized for fast loads and analytic queries aaS ā As a Service ā¢ Like SaaS (e.g. SalesForce.com) ā¢ No infrastructure set up ā¢ Minimal to no administration ā¢ Managed for you by the vendor ā¢ Pay as you go, for what you use
14.
14Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Goals of a Cloud DWaaS Make your life easier ā¢ So you can load and use your data faster Support business ā¢ Make data accessible to more people ā¢ Reduce time to insights Handle big data too! ā¢ Schema-less ingestion
15.
15Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. What to Expect from a Cloud DWaaS It should support standard SQL (natively) ā¢ It should support standard ETL, BI & data science tools ā¢ ODBC or JDBC connectivity It should be infinitly scalable (cloud) ā¢ Handle huge amounts of data ā¢ Handle large number of concurrent queries without performance degradation It should handle flexible schema data types ā¢ No sharding or ETL required
16.
16Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. What to Expect from a Cloud DWaaS It should be secure ā¢ Built in encryption? It shoud be stable ā¢ Resiliancy and availability should be easy to configure and manage It should be easy to configure and manage It should provide a lower TCO ā¢ Cloud scale pricing
17.
17Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Common customer scenarios Data Ā warehouse Ā for Ā SaaS Ā offerings Use Ā Cloud Ā DW as Ā back-Ā end Ā data Ā warehouse Ā supporting Ā data-Ādriven Ā SaaS Ā products noSQL Ā replacement Replace Ā use Ā of Ā noSQL Ā system Ā (e.g. Ā Hadoop) Ā for Ā transformation Ā and Ā SQL Ā analytics Ā of Ā multi-Ā structured Ā data Ā Data Ā warehouse Ā modernization Consolidate Ā legacy Ā datamarts Ā and Ā support Ā new Ā projects
18.
18Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Enabling key use cases Datamart & data silo consolidation Consolidate legacy datamarts to eliminate silos and support new projects Integrated data analytics Directly load structured + semi-structured data into Snowflake for reporting & analytics Data Science, Exploratory & ad hoc analytics Direct access to data for SQL analysts and data scientists to explore data, identify correlations, build & test models
19.
19Ā© 2017 Snowflake
Computing Inc. All Rights Reserved.Ā© 2017 Snowflake Computing Inc. All Rights Reserved. Introducing Snowflake
20.
20Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Snowflake: 1st Data Warehouse Built for the Cloud SQL relational database Optimized storage & processing Standard connectivity ā BI, ETL, ā¦ Data Warehousingā¦ Existing SQL skills and tools āLoad and goā ease of use Cloud-based elasticity to fit any scale Data scientists SQL users & tools ā¦for Everyone
21.
21Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. What is Snowflake? Built for the cloud SQL Data Warehouse Our Vision Provide all users anytime, anywhere insights so they can make actionable decisions based on data Our Solution Next-generation data warehouse built from the ground up for the cloud and for todayās data and analytics Delivered as a service
22.
22Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Snowflakeās differentiating technology Unique architecture: Multi-cluster, shared data Single place for data z Instant, unlimited scalability Zero management Instant, live data sharing
23.
23Ā© 2017 Snowflake
Computing Inc. All Rights Reserved.Ā© 2017 Snowflake Computing Inc. All Rights Reserved. The Data Warriorās Top 10+ Cool Things About Snowflake (A Data Geeks Guide to Cloud-native DW)
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24Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. #10 ā Persistent Result Sets ā¢ No setup ā¢ In Query History ā¢ By Query ID ā¢ 24 Hours ā¢ No re-execution ā¢ No Cost for Compute
25.
25Ā© 2017 Snowflake
Computing Inc. All Rights Reserved.Ā© 2017 Snowflake Computing Inc. All Rights Reserved. #9 -Connect w/JDBC & ODBC to the cloud Data Sources Custom & Packaged Applications & Data Science ODBC WEB UIJDBC Interfaces Java >_ Scripting Reporting & Analytics Data Modeling, Management & Transformation SDDM
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26Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. #8 - UNDROP UNDROP TABLE <table name> UNDROP SCHEMA <schema name> UNDROP DATABASE <db name> Part of Time Travel feature: AWESOME!
27.
27Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. #7 Fast Clone (Zero-Copy) Instant copy of table, schema, or database: CREATE OR REPLACE TABLE MyTable_V2 CLONE MyTable; With Time Travel: CREATE SCHEMA mytestschema_clone_restore CLONE testschema BEFORE (TIMESTAMP => TO_TIMESTAMP(40*365*86400)); PROD PUBLIC Table A Table B Table C DEV PUBLIC Table A Table B Table C PUBLIC Table A Table B Table C INT
28.
28Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. #6 ā JSON Support with SQL Apple 101.12 250 FIH-2316 Pear 56.22 202 IHO-6912 Orange 98.21 600 WHQ-6090 Structured data (e.g. CSV) Semi-structured data (e.g. JSON, Avro, XML) { "firstName": "John", "lastName": "Smith", "height_cm": 167.64, "address": { "streetAddress": "212nd Street", "city": "New York", "state": "NY", "postalCode": "10021-3100" }, "phoneNumbers": [ { "type": "home", "number":"212555-1234"}, { "type": "office", "number": "646 555-4567"} ] } Optimized storage Flexible schema - Native Relational processing select v:lastName::string as last_name from json_demo; All Your Data!
29.
29Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. #5 ā Standard SQL w/Analytic Functions select Nation, Customer, Total from (select n.n_name Nation, c.c_name Customer, sum(o.o_totalprice) Total, rank() over (partition by n.n_name order by sum(o.o_totalprice) desc) customer_rank from orders o, customer c, nation n where o.o_custkey = c.c_custkey and c.c_nationkey = n.n_nationkey group by 1, 2) where customer_rank <= 3 order by 1, customer_rank SQL Complete SQL database ā¢ Data definition language (DDLs) ā¢ Query (SELECT) ā¢ Updates, inserts and deletes (DML) ā¢ Role based security ā¢ Multi-statement transactions New partner post: https://sonra.io/2018/02/04/create-custom-aggregate-udaf-window-functions-snowflake/
30.
30Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. #4 ā Separation of Storage & Compute Snowflakeās multi-cluster, shared data architecture Centralized storage Instant, automatic scalability & elasticity Service Compute Storage
31.
31Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. #3 ā Support Multiple Workloads Accelerate the data pipeline Run loading & analytics at any time, concurrently, to get data to users faster Scale compute to support any workload Scale processing horsepower up and down on-the-fly, with zero downtime or disruption Scale concurrency without performance impact Multi-cluster āvirtual warehouseā architecture scales concurrent users & workloads without contention Deliver faster analytics at any scale Loading Marketing Finance
32.
32Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. ā¢ Elastic scaling for storage Low-cost cloud storage, fully replicated and resilient ā¢ Elastic scaling for compute Virtual warehouses scale up & down on the fly to support workload needs ā¢ Elastic scaling for concurrency Automatically scale concurrency using multi- cluster virtual warehouses Instant, unlimited scalability
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33Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. #2 Ā ā Secure Ā by Ā Design Ā with Ā Automatic Ā Encryption Ā of Ā Data! Embedded multi-factor authentication Federated authentication available Certified against enterprise- class requirements HIPPA Certified! PCI Certified! All data encrypted, always, end-to-end Encryption keys managed automatically NEW: Tri-secret security Role-based access control model Granular privileges on all objects & actions Authentication Access control Data encryption External validation
34.
34Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. #1 - Automatic Query Optimization Zero Management Fully managed with no knobs or tuning required No indexes, distribution keys, partitioning, vacuuming,ā¦ Zero infrastructure costs Zero admin costs
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35Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. New #1 Fav ā Data Sharing (The Data āSharehouseā) Data Consumers Data Providers No data movement Share with unlimited number of consumers Live access Data consumers immediately see all updates Ready to use Consumers can immediately start querying
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36Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Data Sharing - Configuring access create share s1; --empty share grant usage on database sales to share s1; -- add database grant usage on schema sales.east to share s1; -- add schema grant usage on view sales.east.accts to share s1; -- add view alter share s1 add accounts=a1, a2, a3; -- add accounts s1 Provider Database Data Share Object Schema List View List Account List a3 a1 a2 Data provider Data Consumers create database sales from share p1.s1;
37.
Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Enabling Agile DW in the Cloud Examples
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38Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. ā¢Agile Warehouse Scaling ā¢ Separation of Workloads ā¢ Virtual WH Scaling Techniques ā¢Agile Data Lifecycle ā¢ Cloning Enabling the Agile Data Warehouse
39.
Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Agile Ā Warehouse Ā Scaling Separation Ā of Ā Workloads
40.
40Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. ā¢ DWaaS - Snowflake solution ā¢ Assign each workload its own Virtual WH ā¢ At a minimum, two WH: ETL and Business ā¢ ETL can run continuously if it makes sense for the business ā¢ ETL / Business workload contention is eliminated ā¢ Further subdivide business workloads into own clusters as needed ā¢ Eliminate contention between Sales, Marketing, Finance, Data Science workloads ā¢ International groups can operate clusters on their own local time schedules ā¢ Easy to add new workloads on demand! Separation of Workloads Virtual Warehouse Databases Virtual Warehouse ETL & Data Loading Business Workloads Finance Virtual Warehouse Test/Dev Virtual Warehouse S Marketing Virtual Warehouse Sales Virtual Warehouse S Research
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41Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. ā¢ Increase cluster size ā on the fly! ā¢ More data being analyzed ā¢ More complex queries ā¢ Get some concurrency boost ā¢ No need to start, stop, or reboot! ā¢ Automatic scale out! ā¢ Multi-cluster warehouse concurrency ā¢ Workload queries have usual weight ā¢ But more of them, e.g. 20 dashboard users rather than the usual 5 ā¢ Automatically scales back after the load drops Other Agile Warehouse Scaling Techniques
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42Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. ā¢ Anticipated surges ā¢ Explicitly increase WH nodes (T-shirt size) when expecting more data ā¢ Explicitly increase MCWH minimum clusters when expecting more queries ā¢ Can do both at once with ALTER WAREHOUSE ā¢ Use cron or other scheduling/orchestration tool ā¢ Unanticipated surges ā¢ Rely on MCWH maximum clusters for some extra headroom ā¢ Maximize Business Agility! ā¢ Responsiveness for users ā¢ Throughput and value extracted from variable compute power ā¢ Minimize ā¢ Cost and administrative overhead Agile Warehouse Scaling ā Best Practices
43.
Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Agile Ā Data Ā Lifecycle
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44Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. ā¢ Separation of Workloads ā¢ Individual virtual warehouse for each dev/test/prod functional area ā¢ CLONE for dev/test or Data Science Sandbox ā on demand! ā¢ Full logical copy of the data, but uses no extra storage ā¢ Test/dev operations against clone have no effect on original data ā¢ Security ā¢ RBAC limits dev/test access to clone and not production data ā¢ Secure Views permit role- or user-based obfuscation / masking / projection ā¢ Business Impact ā better quality code ā¢ Dev and test teams are working on data at scale, see true app performance ā¢ Full range of values means fewer surprises when app encounters live data Agile Data Lifecycle
45.
45Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. ā¢ Create development (DEV) and integration (INT) databases from production (PROD) Scenario 1 PROD PUBLIC Table A Table B INT PUBLIC Table A Table B DEV PUBLIC Table A Table B CLONE CLONE CREATE OR REPLACE DATABASE DEV CLONE PROD CREATE OR REPLACE DATABASE INT CLONE PROD
46.
46Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. ā¢ Create two new tables, C and D, in the development (DEV) database Scenario 2: new development PROD PUBLIC Table A Table B INT PUBLIC Table A Table B DEV PUBLIC Table A Table B Table C Table D
47.
47Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. ā¢ Mini-release: promote table C for integration testing Scenario 2: new development PROD PUBLIC Table A Table B INT PUBLIC Table A Table B DEV PUBLIC Table A Table B Table C Table D Table C CREATE TABLE LIKE
48.
48Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. ā¢ Deploy to production: promote table C to PROD database Scenario 2: new development PROD PUBLIC Table A Table B INT PUBLIC Table A Table B DEV PUBLIC Table A Table B Table C Table D Table C CREATE TABLE LIKE Table C
49.
49Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. ā¢ Refresh dev: get latest PROD data into DEV and INT Scenario 2: new development PROD PUBLIC Table A Table B INT PUBLIC Table A Table B DEV PUBLIC Table A Table B Table C Table D Table C Table C CLONE DEV PUBLIC Table A Table B Table C Table D DEV2 CLONE CREATE OR REPLACE TABLE Dev2.TableD CLONE Dev.TableD CREATE OR REPLACE DATABASE DEV2 CLONE PROD
50.
50Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. ā¢ CLONE for Data Scientists ā¢ Quick and Safe sandbox for discovery and testing ā¢ Experiment and explore, and write back! ā¢ Test hypothisis and keep iterating ā drop and reclone as needed ā¢ No impact to production data ā¢ Combine with own virtual warehouse for complete isolation ā¢ Business Impact ā better data science ā¢ More fine-grained data over longer time intervals ā¢ Deeper insights, better forecasting, more monetizable results ā¢ CLONE for Compliance ā¢ Monthly, quarterly, annual clones ā financial reporting, auditing requirements ā¢ Business Impact ā simpler compliance ā¢ Your "backups" are live and immediately available Agile Data Lifecycle
51.
Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Snowflake & Spark
52.
52Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Where Snowflake fits with complementary solutions Vast majority, >> 80% or more, is semi-/structured >> 20% or less is unstructured Data Sources for Analytics (Audio, video, text, etc.) (Tables, CSV, JSON, Avro, Parquet, XML, flat files, etc.) Complementary Special data processing (Spark ML, Hadoop lakes, NoSQL front-ends, In-memory DBs, non-relational exploring, et al.) Built for the cloud, relational data warehouse/DB & analytics delivered as a service 52 Use Cases (Tables, metadata) ā¢ Live analytics ā¢ Operational DW ā¢ Real-time dashboards ā¢ Continuous data loading ā¢ Change data capture ā¢ Traditional DW/BI ā¢ Consolidation of data silos ā¢ Relational data exploration ā¢ Interactive queries ā¢ Data science/statistics (Python, R) ā¢ And more ā¢ Spark machine learning ā¢ Unstructureddata mining ā¢ Streaming analytics ā¢ Non-relational data exploration ā¢ Non-relational data science ā¢ And more
53.
53Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Modern Ā Data Ā Architectures Data Production Data Lake Data Consumption Web Servers Data Bases App Metrics Snowflake Business Intelligence Data Science: Exploration Data Science: Machine Learning Model Training Data Augmentation & Loading Data Augmentation & Loading Model-driven Apps Online Recommendations & Behavioral Targeting on Web Pages Offline Churn Prediction Other Machine Learning Apps
54.
54Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Modern Ā Data Ā Architectures: Ā Data Ā Loading Ā & Ā Augmentation Web Servers App Metrics Data Bases Data ProductionCustomers & End Users External Data Augmentation Spark Cluster Kafka SnowSQL COPY Kinesis Transformations and Augmentations in e.g. Python, Scala, SparkSQL, Spark Streaming etc. Snowflake Warehouse Snowflake Spark Connector Full/Pruned Data Lake with Augmentation Snowflake Spark Connector Source/Raw Data Augmented Data Source/Raw Data Augmented Data Transformations and Augmentations using Snowflake SQL Augmentation Augmentation CDC - FiveTran
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55Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Modern Ā Data Ā Architectures: Ā Data Ā Science Jupyter Notebook using e.g. ML Data Exploration Model Design Spark app using Spark ML Model (Re-)Training Operationalized Model (Code + Params) External Data Augmentation Spark Cluster Snowflake Warehouse Snowflake Spark Connector Data Lake with Augmentation Snowflake Spark Connector Source/Raw Data Augmented Data Source/Raw Data Augmented Data Augmentation Augmentation Data Exploration using Snowflake SQL
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56Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Snowflake WarehouseSpark Cluster (e.g. in AWS EMR) AWS S3 3 2 Data Flow: Data Frames Data Flow: Snowflake Tables Master Node Slave Nodes Query (SQL) Cloud Services Virtual Warehouse 1 Architecture: Ā Data Ā Interchange Ā (Read)
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57Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Snowflake WarehouseSpark Cluster (e.g. in AWS EMR) AWS S3 1 3 Data Flow: Data Frames Data Flow: Snowflake Tables Master Node Slave Nodes ControlFlow (SQL) Cloud Services Virtual Warehouse 2 Architecture: Ā Data Ā Interchange Ā (Write)
58.
58Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Spark Cluster Snowflake Virtual Warehouse Super-Charge Spark Processing with Snowflake ā¢ Spark optimizer extension automatically identifies Spark operations with corresponding Snowflake implementations ā¢ Spark connector pushes these operations into Snowflake SQL ā¢ Pushed operations include: project, filter, join, aggregation, limit ā¢ Optimized Snowflake SQL applied to all Spark data frames backed by Snowflake tables ā¢ Transparent to Spark language choice: No need to rewrite Spark (Python, Scala, Java, ā¦) or SparkSQL code df1 = spark.loadā¦(āT1ā) df2 = spark.loadā¦(āT2ā) df1 .join(df2, āidā) .groupBy(āregionā) .count() .collect() SELECT region, count(*) FROM T1 JOIN T2 ON T1.id = T2.id GROUPBY region Snowflake SQL AWS S3 Results
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59Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. ā¢ Reduced complexity, better manageability ā¢ Same data store for curated and raw data ā¢ Same data store for data science and BI data sets ā¢ Fully transactional data store even for raw data when needed ā¢ Performance benefits ā¢ Leveraging structure of the data even in semi-structured data formats such as JSON as compared to flat file storage ā¢ Metadata for pruning based on queries ā¢ Easy to integrate with Spark as compute and application platform ā¢ Overall compute capacity and cost savings due to performance benefits with Snowflake ā¢ Blog posts: ā¢ https://www.snowflake.net/snowflake-and-spark-part-1-why-spark/ ā¢ https://www.snowflake.net/snowflake-spark-part-2-pushing-query-processing/ Snowflake + Spark Benefits
60.
Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. NEW Continous Data Loading with Snowpipe
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61Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Data is created constantly.Data accumulated over time. Data is Being Generated Faster than Ever Before Infrequent Now Constant Web data Corp data App data Thereās an enormous opportunity to use continuously generated data in analysis.
62.
62Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Snowpipe is an automated service that asynchronously listens for new data as it arrives in Amazon Web Servicesā S3 cloud storage service and continuously loads that data into Snowflake. Ā© 2017 Snowflake Computing Inc. All Rights Reserved. Snowflake Confidential ā under NDA only.
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63Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. 6 Data Ingress Approaches & Snowpipe Support Approach Approach Definition Snowpipe Batch Data accumulates over time (hours, days) and is then loaded periodically Option 1: Point at an S3 bucket and a destination table in your warehouse at which point new data is automatically uploaded. Option 2: Technical resources can interface directly with the programmatic REST API along with Java and Python SDKs to enable highly customized loading use cases Micro-batch Data accumulates over small time windows (minutes) and is then loaded Continuous Every data item is loaded individually as it arrives
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64Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Snowpipe Scenario: Automatic Loading from S3 Snowflake Database External S3 SnowPipe Service Server-less Loader S3 notification File data
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65Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Snowpipe ā Current Preview Snowflake Database REST Call {file names} S3 Application SnowPipe Service REST Endpoint Server-less Loader
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66Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Snowpipe Auto-Ingest: Fully Automatic Loading from S3 Snowflake Database External S3 SnowPipe Service Server-less Loader S3 notification File data Use simple Snowflake DDL statements to configure bucket for automatic load with Snowpipe See the video here: https://www.youtube.com/embed/rwZWmcy0aBU?autoplay=1&rel=0 Blog posts: https://www.snowflake.net/your-first-steps-with-snowpipe/ https://www.snowflake.net/snowpipe-serverless-loading-for-streaming-data-2/
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67Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Key Snowpipe Benefits You only pay for the compute time you use to load data Avoid repeated manual COPY commands Continuously generated data is available for analysis in seconds 0 management. No indexing, tuning, partitioning or vacuuming on load Full support for semi-structured data on load Server-less: No servers to manage or concurrency to worry about.
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68Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. ā¢ Server-less billing model: utilization- based billing ā¢ No warehouse to manage for load ā¢ Per core, per second granularity ā¢ Charged as Snowflake credits ā¢ New line item on Snowflake bill ā¢ Components of the Snowpipe charges: ā¢ Loading work ā idle times are not charged ā¢ Time spent in metadata management for file loading 6 Pricing
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69Ā© 2017 Snowflake
Computing Inc. All Rights Reserved.Ā© 2017 Snowflake Computing Inc. All Rights Reserved. Snowflake in Action Today
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70Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. What customers are doing with Snowflake DATA Ā MARTS Ā & Ā EXTRACTS Market research company consolidated data marts to reduce costs and data silos Gaming company replaced Hadoop + SQL database with Snowflake STAGING DATA Ā LAKE DATA Ā WAREHOUSE Consumer retailer modernizing DW by replacing legacy appliance with Snowflake Mobile analytics company shares live data with clients REPORTING, Ā ANALYTICS Ā & Ā APPLICATIONS DATA Ā SOURCES
71.
71Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Delivering compelling results Simpler data pipeline Replace noSQL database with Snowflake for storing & transforming JSON event data Faster analytics Replace on-premises data warehouse with Snowflake for analytics workload Significantly lower cost Improved performance while adding new workloads--at a fraction of the cost noSQL data base: 8 hours to prepare data Snowflake: 1.5 minutes Data warehouse appliance: 20+ hours Snowflake: 45 minutes Data warehouse appliance: $5M + to expand Snowflake: added 2 new workloads for $50K
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72Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Over 1000 customers demonstrating whatās possible Up to 200x faster reports that enable analysts to make decisions in minutes rather than days Ability to load and update data in near real time by replacing legacy data warehouse + Hadoop cluster New applications that provide secure access to analytics to 11,000+ pharmacies
73.
73Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Chosen by leading enterprises Built advertising analytics platform on Snowflake Moving enterprise reporting to Snowflake Deploying BI solution stack on Snowflake Built new embedded analytics application on Snowflake Moved audience reporting infrastructure to Snowflake Replaced Hadoop + legacy data warehouse with Snowflake
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74Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Ranked #1 Cloud Data Warehouse! āSnowflake Hits All the Marksā ā Gigaom 4,85 4,50 4,45 3,75 3,75 3,35 3,20 3,15 2,60 Cloud Analytics Database Distruption Vectors AWS Redshift Oracle Database Exdata Cloud Service SAP HANA Cloud Platform Azure Data Warehouse Vertica DashDB (IBM) Teradata Google Big Query Snowflake RobustnessofSQL15% Built-inOptimization15% On-the-flyElasticity25% DynamicEnvironment Adaption20% SeparationofCompute fromstorage15% SupportforDiversedata 10% Score āYou can tell the data warehouse pedigree from the developmentā¦ With superior performance and the most hands-off model of ownership, Snowflake is the epitome of data warehouse as a service. The model, cost, features and scalability have already caused some to postpone Hadoop adoption.ā William McKnight Gigaom Disruption Vectors Gigaom Analyst Report: Sector Roadmap: Cloud Analytic Databases 2017 Read the full report on snowflake.net
75.
75Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. What does a Cloud-native DWaaS Provide? Cost effective storage and analysis of GBs, TBs, or even PBās Lightning fast query performance Continuous data loading without impacting query performance Unlimited user concurrency Full SQL relational support of both structured and semi-structured data Support for the tools and languages you already useODBC WEB UIJDBCJava >_ Scripting
76.
76Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Big Data does not have to equal Big Effort Web ERP3rd party apps Enterprise apps IoTMobile
77.
77Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. Discover the performance, concurrency, and simplicity of Snowflake As easy as 1-2-3! 01 Visit Snowflake.net 02 Click āTry for Freeā 03 Sign up & register Snowflake is the only data warehouse built for the cloud. You can automatically scale compute up, out, or downāindependent of storage. Plus, you have the power of a complete SQL database, with zero management, that can grow with you to support all of your data and all of your users. With Snowflake On Demandā¢, pay only for what you use. Sign up and receive $400 worth of free usage for 30 days!
78.
Kent Graziano Snowflake Computing Kent.graziano@snowflake.net On
Twitter @KentGraziano More info at http://snowflake.net Visit my blog at http://kentgraziano.com Contact Information
79.
79Ā© 2017 Snowflake
Computing Inc. All Rights Reserved. YOUR DATA, NO LIMITS Ā© 2017 Snowflake Computing Inc. All Rights Reserved. Thank You!
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