Beyond Lambda - how to get from logical to physical. Artur Borycki, Director International Technology & Innovations

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1 Beyond Lambda - how to get from logical to physical Artur Borycki, Director International Technology & Innovations

2 Simplification & Efficiency Teradata believe in the principles of self-service, automation and on-demand resource allocation. These enable faster, more efficient and more effective data application development and operation.

3 What is Lambda Architecture Background Reference architecture for Big Data systems Designed by Nathan Marz (Twitter) Defined as a system that runs arbitrary functions on arbitrary data query = function(all data) Design Principles Human fault-tolerant, Immutability, Computable Lambda Layers Batch - Contains the immutable, constantly growing master dataset. Speed - Deals only with new data and compensates for the high latency updates of the serving layer. Serving - Loads and exposes the combined view of data so that they can be queried. #

4 Active Executor Lambda Framework The way this works is that an immutable sequence of records is captured and fed into a batch system and a stream processing system in parallel. You implement your transformation logic twice, once in the batch system and once in the stream processing system. You stitch together the results from both systems at query time to produce a complete answer. #

5 Overall Architecture Customer example

6 Lambda alternative Kappa? (Jay Kreps Linkedin) Unlike the Lambda Architecture, in this approach you only do reprocessing when your processing code changes 1. Use Kafka or some other system that will let you retain the full log of the data you want to be able to reprocess and that allows for multiple subscribers. For example, if you want to reprocess up to 30 days of data, set your retention in Kafka to 30 days. 2. When you want to do the reprocessing, start a second instance of your stream processing job that starts processing from the beginning of the retained data, but direct this output data to a new output table. 3. When the second job has caught up, switch the application to read from the new table. 4. Stop the old version of the job, and delete the old output table. #

7 Real-time Maturity Typical path for a customer Customers typically go through four stages on their path to real-time analysis Event Automation Need to react without human intervention. The evolution typically starts with trying to visualize results or reports more frequently. This leads to the realization that the underlying data is not refreshed frequently. The next stage of maturity is to capture and ingest data more quickly. Once data is flowing faster, customers then try to process the data as it is flowing. The final stage is to remove any human intervention. Event Processing Need to process and/or analyze data more quickly. Faster Ingest Need to acquire data more quickly. Faster Results Need to visualize data more quickly.

8 Beyond Lambda Omega ;) (Artur vision) Consumer of Information Events / Interactions Streams Other feeds All data Discovery Data binding Advance Analytics Reporting We need events that require actions and interactions without much of the analytics We need events that are requiring action, but also they need to be enhanced by the analytics in the ecosystem (based on other information sources) We need events that will be handled later or they are supporting above cases Consumer of Information #

9 The Teradata UDA UNIFIED DATA ARCHITECTURE REAL TIME Security, PROCESSING Workload Management INTEGRATED DATA WAREHOUSE Applications RESTFUL API LISTENING FRAMEWORK DATA PLATFORM TERADATA PORTFOLIO FOR HADOOP TERADATA DATABASE INTEGRATED DISCOVERY PLATFORM RESTFUL API APP FRAMEWORK TERADATA ASTER DATABASE #

10 Decoupled Services 1 0 Instead of a single monolithic database Monolith A monolithic application puts all of its functionality into a single process and scales by replicating the monolith on multiple servers. Microservices A microservices architecture puts each element of functionality into a separate service and scales by distributing these services across servers.

11 Think Microservice, not Monolithic 1 1 Scale by distributing services and replicating as needed Monolithic App A monolithic application puts all of its functionality into a single process and scales by replicating the monolith on multiple servers. Microservices A microservices architecture puts each element of functionality into a separate service and scales by distributing these services across servers.

12 The Data Lake Customer slide This is not skating to where the puck is going to be - It s skating to the puck. Your CIO should be sitting you on the bench if you are not doing this already Most Data Lakes Today Passive cheap storage Really only using HDFS Limited data governance Staging Data Archiving Data DW offload (cost drivers) The Data Lakes we Should be Building Active balanced nodes Using full Hadoop stack+ Good data governance Good information architecture Processing and enhancing data Data applications (flexibility drivers) 12

13 New Architecture Architecture Information architectures are distributed Focus on data and business questions, not integrating separate systems Application architectures are variable Don t force applications into a single architecture Applications are Loosely Coupled DW is an application BI is an application (or many) Data applications are everywhere! But let s be smart about it Still need strong information architecture and data management practices Still need to reduce complexity and make strategic choices on technology 13

14 Loosely Coupled Data Applications BI Application General Query and Access to Features Teradata SQL, Microstrategy, Tableau Feature Store Application Generating Analytical Features HDFS, Spark, ElasticSearch Customer Matching Application Associating Customers Spark, Python, Scala, R Customer Registry Application Registry Model of all Customers Teradata Campaign Application Targeting Customers Teradata Teradata

15 Teradata Customer - Microservices Example

16 Customer example Integration Flow User starts a Workflow from the UI which has a single Pig Job. Azkaban Web requests that the Azkaban Executor start a new Pig Job. Pig Job makes a REST call to the TemplateModule to render the Pig Template. TemplateModule fetches config values from the ConfigModule if needed by the template. The ConfigModule in turn fetches config values either from the PCF Data Schema or from external systems. TemplateModule renders the Pig Template and returns a complete Pig Script. Pig Job executes the Pig Script against the Hadoop cluster. During the Pig Job execution it makes REST calls to the EventModule informing about its progress. As the Job progress is updated Vertx updates the Azkaban Web UI in real time. When the Pig Job has completed it makes a REST call to the AuditModule to log its completion. The AuditModule in turn stores auditing information in the PCF Data Schema. Finally the Pig Job returns its execution status back to the Azkaban Executor. 16

17 17 Customer Event Flow

18 18 Customer Docker services

19 Easily Access UDA Tap into the power of the platform without duplicating effort AUTH SERVICES TD DATA SERVICES ASTER DATA SERVICES HIVE DATA SERVICES MICRO SERVICES FRAMEWORK YOUR ANALYTIC APP

20 UDA & the LDA 2 0 Extract, Load & Transform in the Layered Architecture Level 0 Staging 1:1 Source Systems LISTENER EXTRACT LOAD Level 1 Level 2 Level 3 Integration Integrated Model at Lowest Granularity Calculation Key Performance Indicators Aggregation Business Unit Specific Rollups WORKLOAD ANALYTICS BUSINESS HEALTH MEMBER SEGMENT ENGINE DAILY FINANCIALS APP CENTER CATEGORY SALES TRANSFORM #

21 Teradata Listener Teradata Listener provides the foundation on which enterprise real-time analytics will be made available

22 Listener Scope 2 2 Focused on simplicity and efficiency for acquiring level 0 data Level 5 Level 4 Level 3 Level 2 Level 1 Level 0 Datalab Virtual Sandboxes & Prototypes Presentation Application Specific Views Aggregation Business Unit Specific Rollups Calculation Key Performance Indicators Integration Integrated Model at Lowest Granularity Staging 1:1 Source Systems Developer Self-Service Developers should be able to register data sources and send data with minimal friction. Minimal Transformation The principle job is to acquire and persist data into level 0 of the layered data architecture. Speed & Scale Tolerant The system should gracefully handle bursts of data, scale horizontally and store data quickly.

23 Listener Architecture 2 3 Self-service platform for landing data STORAGE API Services Ingest Services Listener Data Writers Cache Data Sources Target Systems

24 Use Cases Web-click Data Collection Easily instrument and capture all webclick data for detailed analysis. Combine web-click data with traditional data for a holistic view of customers. Data Enrichment Enrich existing data with public data streams such as Twitter, etc. IoT/Sensors Collect real-time streaming data from sensors and other devices. Application and System Data Collection With Teradata Listener, application developers can easily instrument their applications for analysis during development and operational use. Automatically make instrumentation part of the development life cycle. Log/ETL Buffer Teradata Listener provides a common point for collecting and pushing out data across the enterprise. Instead of building point-to-point extraction connectors Listener can be used as a common integration point.

25 Summary - The Teradata UDA UNIFIED DATA ARCHITECTURE REAL TIME Security, PROCESSING Workload Management INTEGRATED DATA WAREHOUSE Applications RESTFUL API LISTENING FRAMEWORK DATA PLATFORM TERADATA PORTFOLIO FOR HADOOP TERADATA DATABASE INTEGRATED DISCOVERY PLATFORM RESTFUL API APP FRAMEWORK TERADATA ASTER DATABASE #

26 26 THANK YOU

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