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December 17, 2024 Informatica and the Modern Cloud Data Architecture
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Victoria Hyde-Dunn Vice President Investor Relations Welcome
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3 Forward-Looking Statements This presentation and the accompanying oral commentary have been prepared by Informatica for informational purposes only and not for any other purpose. Nothing contained in this presentation is, or should be construed as, a recommendation, promise, or representation by the presenter or Informatica any officer, director, employee, agent, or advisor of Informatica . This presentation does not purport to be all-inclusive or to contain all of the information you may desire. Information provided in this presentation and the accompanying oral commentary speak only as of the date set forth on the cover page of this presentation . Forward-Looking Statements This presentation contains forward-looking statements about Informatica and the environment in which Informatica operates. These statements may relate to, but are not limited to, expectations of future operating results or financial performance, market size and growth opportunities, the calculation of certain of our key financial and operating metrics, capital expenditures, plans for future operations, competitive position, technological capabilities and new product releases, including those that use artificial intelligence and machine learning, our efforts to reduce operating expenses and adjust cash flows in light of current business needs and priorities, the effect of macro-economic conditions, and strategic relationships, as well as assumptions relating to the foregoing. Such statements are subject to known and unknown uncertainties and contingencies outside of Informatica’s control and are largely based on our current expectations and projections about future events and financial trends that we believe may affect our financial condition, results of operations, business strategy, and financial needs. Informatica’s actual results, events, or circumstances may differ materially from these statements . Forward-looking statements include all statements that are not historical facts and can be identified by terms such as “anticipate,” “believe,” “contemplate,” “continue,” “could,” “estimate,” “expect,” “intend,” “may,” “plan,” “potential,” “predict,” “project,” “should,” “target,” “will,” “would,” or similar expressions and the negatives of those terms. You should not put undue reliance on any forward-looking statements . Forward-looking statements should not be read as a guarantee of future performance or results and will not necessarily be accurate indications of the times at, or by, which such performance or results will be achieved, if at all. Forward-looking statements are based on information available at the time those statements are made and/or management’s good faith beliefs and assumptions as of that time with respect to future events and are subject to risks and uncertainties that could cause actual performance or results to differ materially from those expressed in or suggested by the forward-looking statements . In light of these risks and uncertainties, the forward-looking events and circumstances discussed in this presentation may not occur, and actual results, performance or achievement could differ materially from those anticipated or implied in the forward-looking statements . Moreover, we operate in a very competitive and rapidly changing environment . New risks and uncertainties emerge from time to time and it is not possible for us to predict all risks and uncertainties that could have an impact on the forward-looking statements contained in this presentation . Except as required by law, Informatica does not undertake any obligation to publicly update or revise any forward-looking statement, whether as a result of new information, future developments or otherwise. Further information on these and additional risks, uncertainties, and other factors that could cause actual outcomes and results to differ materially from those included in or contemplated by the forward-looking statements contained in the earnings release issued on October 30, 2024 are included under the caption “Risk Factors” and elsewhere in our Quarterly Report on Form 10-Q that was filed for the third quarter ended September 30, 2024, and other filings and reports we make with the Securities and Exchange Commission ("SEC") from time to time, including our Annual Report on Form 10-K that was filed for the fiscal year ended December 31, 2023. This presentation contains statistical data, estimates and forecasts that are based on independent industry publications or other publicly available information, as well as other information based on our internal sources. This information involves many assumptions and limitations, and you are cautioned not to give undue weight to these estimates . We have not independently verified the accuracy or completeness of the data contained in these industry publications and other publicly available information . Accordingly, we make no representations as to the accuracy or completeness of that data nor do we undertake to update such data after the date of this presentation .
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4 Informatica Product Disclaimer Statement The information being provided herein is for informational purposes only. The development, release and timing of any Informatica product, service or functionality described herein remain at the sole discretion of Informatica and should not be relied upon in making a purchasing decision. Statements made herein are based on information currently available, which is subject to change. Such statements should not be relied upon as a representation, warranty or commitment to deliver specific products or functionality in the future. Actual products, services or functionality may differ materially from those expressed or implied as a result of various risks and uncertainties. For more information about some of these risks, please review the company’s SEC filings, including the section titled Risk Factors.
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5 Today’s Topics 1 Introduction Michael McLaughlin, Chief Financial Officer 2 Informatica Data Integration & Engineering in the Cloud • History of Data Warehousing • Informatica in the Modern Data Architecture • Hot Topics: Open Tables, Zero ETL, Data for AI Pratik Parekh, SVP, GM, Cloud Data Integration, Modernization and iPaaS 3 Informatica’s Role in Cloud Data Governance Brett Roscoe, SVP, GM, Governance and Cloud Ops Submit Questions investors@informatica.com 4 Wrap-up and Q&A
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Chief Financial Officer Introduction Michael McLaughlin
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7 What drove the need for ‘data management’ in the first place?
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8 1993
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9 Data Management in the On-Premises World On-Prem Data Warehouse On-Premises Data Sources Data Users Data Pipelines Analytics Reporting Business Intelligence ERP CRM SCM HR E-com Custom KEY POINT: Compute and storage provided by customer
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10 Informatica’s Total Revenues 1993 2015 $ $1.05B $0 Total revenues for full -year 2014 as filed in Form 10-K dated February 26, 2015.
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11 Informatica’s Total Revenues Growth 1993 2015 $ On-Premises Data Management Competition $1.05B $0 Total revenues for full -year 2014 as filed in Form 10-K dated February 26, 2015.
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12 22 Years Later… 2015
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13 Informatica’s Response Take private in 2015 Began developing a cloud-native, true-platform for cloud data management Clean sheet of paper, using cutting-edge architecture and building blocks $1B+ in R&D ✓ ✓ ✓ ✓
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14 The Result: The Intelligent Data Management Cloud
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15 The Result: The Intelligent Data Management Cloud Only Data Management Platform Best Data Management Products Multi-Vendor, Multi-Cloud & Hybrid
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16 The Result: Market Recognition G a rtner, M ag i c Quadr ant fo r A u gm en ted Data Quality Soluti o ns, M el o dy Chien, et al., 6 March 2024; G a rtner, M ag i c Quadr ant fo r Integrati o n Platform as a Servi ce, K eith G uttridg e, et al., 1 9 F eb 2024; Gartn er, M agic Q uad rant f or D ata I n tegr ation Tools, Thornton Craig, et a l ., 3 D ec 2024, GA RTNER i s a r egistered tradem ar k and serv ice m ark of G ar tner, I nc. and/o r i ts aff i liates in the U.S. and interna tionally , and MAG IC QU ADR ANT is a reg i ster ed tr ademark of Gartner , Inc. and/o r its aff il iates and ar e used h erei n with p ermi ssion. Al l rights reserved. G a rtner does n ot endo rse an y v endor, pr oduct o r ser v ice depicted i n its r esearch p ublication s and does n ot advi se technology user s to sel ect only those vend ors with the h i g hest r atings o r other design ation. G ar tner research publicati o ns consist of th e opinions of Gartner’s r esearch o rganiz ation and sh ould not be co nstr ued as statem en ts of fa ct. G ar tner disclaim s a l l war ranti es, expressed or impli ed, with respect to this resear ch, including any warr anties o f m er chantability or f i tness for a par ticular pu rpose. This gr aphic was pu bli shed by Gartner , Inc. as par t of a larger research document an d should be evaluated in the con text of the entire d oc um ent. The G artner docu m ent is available upon r equest fr om I n formatica. The For rester Wav e is copyrighted by Fo rrester R esear ch, Inc. Forr ester and For rester Wav e are tradem ar k s of Fo rrester R esear ch, Inc. T h e Forr ester W ave is a graph i cal r epr esentati o n o f Forr ester’s call on a m ar k et and is plotted using a detail ed spreadsh eet with ex po sed scores , weigh tings, and com m ents. For rester d oes not endor se any v endor , pr oduct, or ser v ice d epicted in the For rester Wav e . Inf orm a tion is based o n b est available resour ces. Opinions reflect jud gm en t at the ti me and are subject to chan ge. Only Data Management Platform Best Data Management Products Multi-Vendor, Multi-Cloud & Hybrid Sour ce: ID C M a rk etSca pe: Worldwide Data Intelli g ence Platform So ftwa re 2024 Ven dor Assessment, By: Stewart Bon d, M a rlanna Boz icev ich, Decem ber 2 024, I DC #US514 67224. ID C M a rk etSca pe v endor an aly sis model is design ed to pro v ide an overview of th e com p etitive fitness of ICT su ppliers in a giv en market. The re search metho dology utiliz es a rigoro us sco ring m ethodolog y based on both qua l itativ e a nd quanti tative criteria that r esu l ts in a si n gle graph i cal illustr ation of each v endor ’s position within a given m ar k et. The Capa bil iti es sco re m easur es v endor pr oduct, go-to-m ar k et and business execu tion in the sho rt -term . The Stra tegy scor e m ea sures alignm ent of v endor strategies with custo m er r equirem en ts in a 3-5-y ear tim efra m e. Vendor market sh are is represented by th e siz e of the circles. Vendor year-over-y ear gro wth rate relativ e to the giv en market is indicated by a plus, neutr al or minus next to th e v en dor name.
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17 The more things change, the more they stay the same… Cloud Data Warehouse / Lake / Lakehouse Cloud & On-Prem Data Sources Cloud Data Users Data Pipelines Analytics Reporting Business Intelligence SAP S4/HANA Salesforce Oracle Fusion Workday E-com Custom Data Science AI “ETL” “ELT” “Reverse ETL” KEY POINT: Compute and storage provided by customer
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18 New Paradigm, More Complexity Cloud Data Warehouse / Lake / Lakehouse Cloud & On-Prem Data Sources Cloud Data Users Data Pipelines Analytics Reporting Business Intelligence SAP S4/HANA Salesforce Oracle Fusion Workday E-com Custom Data Science AI IoT CDP HCM SEO
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19 Most Organizations Use Data Warehouses and Operational Data; Not One, But Many Q. Which of the following types of repositories are used to make data available for analysis? Q. How many analytical data repositories does the organization currently manage? Mean number of analytical repositories 18.3 Analytic repositories Number of analytical repositories Notes: High scoring organizations are much more likely to leverage data lakes compared with low scoring organizations. Variations of means across data management score levels aren't significant. Source: IDC, “2024 Office of the CDO Survey Technical Perspectives: Data Challenges Influence Technology Investments and Outcomes,” Doc #US51070723, December 2024. (n = 848 total, n = 200 low, n = 156 high) Operational reporting stores Data lakehouses Data lakes Data warehouses Total High Score Low Score 65% 60% 74% 31% 57% 6% 32% 40% 19% 53% 48% 61% (% of respondents)
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20 Forrester, Public Cloud Market Insights, 2024 https://www.forrester.com/report/public -cloud-market-insights-2024/RES181652
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21 Forrester, Public Cloud Market Insights, 2024 https://www.forrester.com/report/public -cloud-market-insights-2024/RES181652 Only Data Management Platform Best Data Management Products Multi-Vendor, Multi-Cloud & Hybrid
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22 Informatica’s Cloud Growth (Cloud Subscription ARR) Q3 2024 $ $748M 2015 As of September 30, 2024
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23 Today’s Topics Submit Questions investors@informatica.com 1 Introduction Michael McLaughlin, Chief Financial Officer 2 Informatica Data Integration & Engineering in the Cloud • History of Data Warehousing • Informatica in the Modern Data Architecture • Hot Topics: Open Tables, Zero ETL, Data for AI Pratik Parekh, SVP, GM, Cloud Data Integration, Modernization and iPaaS 3 Informatica’s Role in Cloud Data Governance Brett Roscoe, SVP, GM, Governance and Cloud Ops 4 Wrap-up and Q&A ✓
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Pratik Parekh SVP, GM, Cloud Data Integration, Modernization and iPaaS Informatica Data Integration & Engineering in the Cloud Data Warehousing, Data Lake, Lakehouse
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25 Informatica in the Traditional Data World
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26 “Data As-Is” is Not Ready for Analytics Decoding transactional data is challenging Customer CustomerID(PK) Fname Lname Address Phone Email OrderHeader OrderID(PK) Order Date Order Time CustomerID OrderLine OrderID(PK) LineID(FK) ProductID Quantity Delivery DeliveryID(PK) OrderID Type Status Departure Arrival Payment PaymentID(PK) Pdate Type OrderID CustomerID Total Product ProductID(PK) Name Specification
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27 Analytics – Sample BI Dashboard On time delivery of vehicles by vehicle type and region 90 Cars-All 83 Trucks 87 Overall 0 2 4 6 8 10 12 14 Cars- Gas Cars- Electric Cars-Diesel Trucks Deliveries by Vehicle Type US EMEA APAC 58% 23% 10% 9% Cars- Gas Cars- Electric Cars-Diesel Trucks Current Quarter Deliveries (US regions) Region On=time % Top Delay Reasons West 95 East 90 October Hurricane Central 98 0 2 4 6 Q1 Q2 Q3 Q4 US Material Deliveries by Quarter East West Central Region Plant Total $$M Comments US- West Arizona 50 Excludes returns US- Central Austin 10 US- East Albany 10 EU North Frankfurt 23 In USD EU-South Madrid 8 In USD Summary for all regions On-time Summary for US regions
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28 Analytics Requires Data Preparation Transforming data is foundational for meaningful analytics Receipt No. Receipt Dt. Order No. Part ID Plant ID Qty. UOM Order Type Tax Type Other Data R01 5/1/2024 1001 PBX202 USE001 100 GAL D US1 --- R02 5/10/2024 1002 ACC001 EUR200 1 EA 1 EU1 --- Receipt No Receipt Dt Order No Order Dt Customer Name Part Name Plant Id Qty UOM Tax Type Other Data R01 5/1/2024 1001 1/1/2024 Auto Parts Engine Oil New York 01 100 GAL US Direct Material --- R02 5/10/2024 1002 5/1/2024 US Cables Cable 10 Frankfurt 1 EA EU Direct Material --- Join related tables Filter unwanted records Calculate amounts Cleanse data Convert dates Standardize reference values Resolve codes Summarize line level data Resolve surrogate keys Calculate complex metrics Map to analytics model Mask PII values Apply business rules Version source data Perform conditional updates
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29 Traditional Data Warehousing – Architecture Compute and storage in one logical system On-Prem Data Warehouse Appliances Analytics Apps Query Data Processing Optimized Storage
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30 Silver GoldBronze Analytics Apps Query Data Processing Optimized Storage Informatica’s Role in Traditional Data Warehousing End-to-end on-prem data pipeline management Extract, Transform & Load (ETL) Data Formats, Patterns & Latencies Real-time Events Batch ELT PowerCenter Data Quality Data Catalog Master Data Management Transactional Systems
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31 Competitive Landscape in 2015 Competition involved pure play vendors as well as data warehousing and analytics vendors Source: Gartner ® Magic Quadrant for Data Integration Tools 2015 Gartner®, Magic Quadrant for Data Integration Tools, Eric Thoo, Lakshmi Randall, 29 July 2015, GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Informatica.
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32 Key Value Proposition of Informatica End-to-end on-prem data pipeline management High Productivity Development Workload Optimization Data and Platform Portability Future Proof Enterprise Grade Operationalization
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33 25% 50% 25% Traditional Data Warehousing - Economics Spend on Informatica has always been independent of analytics infrastructure Silver GoldBronze Analytics Apps Query Data Processing Optimized Storage Extract, Transform & Load (ETL) Data Formats, Patterns & Latencies Real-time Events Batch Transactional Systems PowerCenter ELT ERP Mainframe CRM 15-20% Note: Numbers are representative approx. with broadly understood view of IT spend for analytics including data warehousing, analytics software, underlying compute and storage.
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Informatica in the Modern Cloud Data World
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35 Emergence of Cloud Flexibility, agility and economies of scale IaaS Infrastructure as a Service PaaS Platform as a Service SaaS Software as a Service
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36 Complexity of Modern Data and Applications Significant increase in data silos and fragmentation Data Latencies Batch Data Real-time Event Based Streaming Applications / SaaS ERP SaaS IoTCRM Social Web API Services 1000+ applications are used in most enterprises 149 zettabytes of data are created and growing over 40% every year Data Stores Message Queue FilesMainframe No SQLRelational Big Data Vector Databases Source: Salesforce Third Edition State of IT Report 2023 | Source: Statista Survey 2024 Data fragmentation leads to flawed analysis and insights leading to poor decision- making; further elevating the need for good data integration and data management Data Structures Parquet/Avro /ORC Structured Data Semi- Structured Data Unstructured Data XML JSON Graph Data
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37 Data Formats, Patterns & Latencies Real-time Events SaaS Semi- structured data Transactional Systems Cloud Data Integration Analytics Apps Query Data Processing Optimized Storage Data Lake - S3 / ADLS / GCS Data Science Apps Silver GoldBronze Informatica’s Role in Modern Data Warehousing ELT Decoupling of compute and storage
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38 Gartner’s Point of View on Data Integration Informatica offers the most complete and comprehensive DI and DE capabilities “The data integration tools market remains buoyant as organizations increasingly seek improved capabilities to support their operational, analytical and AI infrastructure use cases. Data integration functions enable ingestion, sharing and consumption of data across all organizational and systemic boundaries. Therefore, organizations are increasingly seeking a comprehensive range of improved data integration capabilities to modernize their data, analytics and AI infrastructures.” Source: 2024 Gartner® Magic Quadrant for Data Integration Tools Bulk / Batch Data Movement Advanced Data Transformation Data Governance Support Data Replication & Synchronization Data Preparation DataOps Support Stream Data Integration Augmented Data Integration FinOps Support Data Virtualization Metadata Management Support Deployment Options Data Engineering Delivering Modern Data Management Architectures Enabling Less- Technical Data Integration Operational Data Integration ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Gartner®, Magic Quadrant for Data Integration Tools, Thornton Craig, et al., 3 Dec 2024, GARTNER is a registered trademark an d service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Informatica.
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39 Competitive Landscape in 2024 Gartner®, Magic Quadrant for Data Integration Tools, Thornton Craig, et al., 3 Dec 2024, GARTNER is a registered trademark an d service mark of Gartner, Inc. and/or its affiliates in the U.S. an d intern ationally, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved. Gartner does not en dorse any vendor, product or service depicted in its research publication s and does n ot advise tech nology users to select only th ose vendors with the highest ratings or other designation. Gartner research pu blications con sist of th e opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness f or a particular purpose. This graphic was pu blished by Gartner, Inc. as part of a larger research document and should be evaluated in the con text of the entire document. Th e Gartner document is available upon request from Informatica. Competition includes pure play vendors as well as cloud data warehousing and platform vendors
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40 Key Value Proposition of Informatica High Productivity Development Workload Optimization Data and Platform Portability Future Proof Enterprise Grade Operationalization End-to-end cloud data pipeline management
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41 High Productivity Development Intuitive design experience with out of box ready integrations • Complex and error prone data pipeline development process • Difficult to maintain for enterprise scale (thousands of integration assets) • Specialized programming skills are required for handcoding • Shortage of skills 10X Improvement in developer productivity Source: Informatica IPS and System Integrator Study, 2023 Vs.
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42 Workload Optimization Choice of deployment and Smart Optimization of integration workloads Elastic Serverless 7.5X Cost Performance Benefit Informatica Optimizer Hybrid/ On-Premises Source: INFA -FinOps for Cloud Data Integration Whitepaper published in IEEE Big Data 2024 Within CDW
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43 REST PostgreS QL Marketin g Cloud A t hena A ur ora A ur ora My SQ L A ur ora PostgreSQ L D ynamoD B K inesi s Firehose K inesi s Streams R DS M yS QL (Community ) R DS O r acle R DS PostgreSQ L R DS SQ L S e rv er R e dshift S3 SNS SQ S C loud Storage Bi gQuery Google Bi gTable Google C loud Pub/ Sub Google Looke r Google A nalytics Google Sheets Google D rive O bject Storage A ut onomous D atabase O rac l e D atabase O rac l e DB C loud Serv ice A zure B lob Storage A zure D ata Factory A DLS Ge n1 A DLS Ge n2 A zure Serv ice Bus A zure S QL D atabase A zure S QL DW A zure S QL Serv er A zure Sy napse Script A zure Sy napse SQ L C DM F older s Fabric O neL ake Fabric DW A zure C osm osD B A zure Ev entHub CDP Data and Platform Portability 50,000+ metadata-aware connections (”Switzerland of Data”)
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44 Future Proof Business logic retained across technology, platform and architecture shifts 1990 2000 2010 2020 2025 Data Marts Data Warehouses Hadoop/Big Data Cloud-Native Data Data Lakes, Data Warehouses & Lakehouses GenAI
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45 Hybrid 99.9% SLA for all customers +101T Cloud transactions per month* *as of September 30, 2024 Scalable Security Global Reach Continuous Integration/ Continuous Deployment Enterprise Grade Operationalization Scale-ability, availability, reliability, manage -ability and security
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46 Key Value Proposition of Informatica High Productivity Development Workload Optimization Data and Platform Portability Future Proof Enterprise Grade Operationalization End-to-end cloud data pipeline management
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47 Best Price Performance in the Cloud Source: Paycor Customer Case Study on informatica.com 50% Cost Reduction 5X Faster Performance Empowering organizations for faster and better decision-making Switched to Informatica Cloud Data Integration from competing hyperscaler solution. Benefits included: 1. Simplicity and ease of use 2. No code UI for high productivity and low maintenance 3. Flexibility and scale to build a wide range of solutions without the need for deep technical expertise Customer case study
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48 Best Price Performance in the Cloud Internal Informatica benchmarking 42% Cost Reduction 4.5X Faster Performance Cloud Data Warehouse Integration Patterns 0:00:00 0:07:12 0:14:24 0:21:36 0:28:48 0:36:00 0:43:12 0:50:24 $16.00 $14.00 $12.00 $10.00 $8.00 $6.00 $4.00 $2.00 $0.00 CDW to CDW Use Case 1 CDW to CDW Use Case 2 CDW to CDW Data filter CDW to CDW Aggregation case Competitor 0:42:03 0:11:50 0:24:20 0:10:11 CDI 0:14:16 0:02:03 0:03:56 0:00:18 Competitor Cost $14.02 $3.95 $8.11 $3.40 CDI Cost $12.65 $1.82 $3.49 $0.27 Informatica Benchmark November 2023
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49 Data Formats, Patterns & Latencies Real-time Events SaaS Semi- structured data Transactional Systems Cloud Data Integration Analytics Apps Query Data Processing Optimized Storage Data Lake - S3 / ADLS / GCS Data Science Apps Silver GoldBronze ELT Modern Data Warehousing - Economics Spend on Informatica has always been independent of analytics infrastructure Declining unit cost of compute and storage
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Emerging Trends in Cloud Data Integration
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51 Modern Data Warehousing - Emerging Trends Open Table Formats Zero Copy/ Zero ETL AI
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52 Open Table Formats Modern Data Warehousing - Emerging Trends
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53 Evolution of Open Standards Driven by changing needs, new challenges and technological advancements 1998 2001 2006 2008 2009 2013 2016 2018 2019 2022 2023
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54 Modern Data Warehousing Analytics Apps Query Data Processing Optimized Storage Data Lake - S3 / ADLS / GCS Data Science Apps ELT Silver GoldBronze Data Formats & Patterns Real-time Events SaaS Semi- structured data Transactional Systems Cloud Data Integration SaaS Applications ERP CRM
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55 Analytics Apps Query Data Processing Open Table Data Lake - S3 / ADLS / GCS Data Science Apps ELT Silver GoldBronze Data Formats & Patterns Real-time Events SaaS Semi- structured data Transactional Systems Cloud Data Integration SaaS Applications ERP CRM Modern Data Warehousing - Open Table Formats Iceberg, Delta, Hudi for large analytical workloads – Positive for Informatica
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56 Zero Copy/ Zero ETL Modern Data Warehousing - Emerging Trends
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57 Analytics Apps Query Data Processing Optimized Storage Data Lake - S3 / ADLS / GCS Data Science Apps ELT Silver GoldBronze Data Formats & Patterns Real-time Events SaaS Semi- structured data Transactional Systems Cloud Data Integration SaaS Applications ERP CRM Modern Data Warehousing - Zero Copy/Zero ETL
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58 Analytics Apps Query Data Processing Optimized Storage Data Lake - S3 / ADLS / GCS Data Science Apps ELT Silver GoldBronze Data Formats & Patterns Real-time Events SaaS Semi- structured data Transactional Systems Cloud Data Integration SaaS Applications ERP CRM Modern Data Warehousing - Zero Copy/Zero ETL
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59 Modern Data Warehousing - Zero Copy/Zero ETL Analytics Apps Query Data Processing Data Science Apps Analytics without copying and transformation. Is that true? Transactional Systems SaaS Applications ERP CRM
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60 Modern Data Warehousing - Zero Copy/Zero ETL Analytics Apps Query Data Processing Data Science Apps ConfLeads.csv Marketing Analyst What is the use case? Ad hoc look up and query Example: Leads generated at a conference ? How many people attend the conference? ? How many customers? ? How many opt-ins? Transactional Systems SaaS Applications ERP CRM
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61 Modern Data Warehousing - Zero Copy/Zero ETL Analytics Apps Query Data Processing Data Science Apps But, what about these questions? Example: Leads generated at a conference How much pipeline is generated?? How many opportunities are created? ? How is the campaign performing? ? What is the sales forecast?? Good for ad-hoc data lookup and inquiry Transactional Systems SaaS Applications ERP CRM
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62 Analytics Requires Data Preparation Transforming data is foundational for meaningful analytics Receipt No. Receipt Dt. Order No. Part ID Plant ID Qty. UOM Order Type Tax Type Other Data R01 5/1/2024 1001 PBX202 USE001 100 GAL D US1 --- R02 5/10/2024 1002 ACC001 EUR200 1 EA 1 EU1 --- Receipt No Receipt Dt Order No Order Dt Customer Name Part Name Plant Id Qty UOM Tax Type Other Data R01 5/1/2024 1001 1/1/2024 Auto Parts Engine Oil New York 01 100 GAL US Direct Material --- R02 5/10/2024 1002 5/1/2024 US Cables Cable 10 Frankfurt 1 EA EU Direct Material --- Join related tables Filter unwanted records Calculate amounts Cleanse data Convert dates Standardize reference values Resolve codes Summarize line level data Resolve surrogate keys Calculate complex metrics Map to analytics model Mask PII values Apply business rules Version source data Perform conditional updates
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63 Modern Data Warehousing Zero Copy/Zero ELT does not impact Informatica: Data Integration is required for enterprise analytics Analytics Apps Query Data Processing Optimized Storage Data Lake - S3 / ADLS / GCS Data Science Apps ELT Silver GoldBronze Data Formats & Patterns Real-time Events SaaS Semi- structured data Transactional Systems Cloud Data Integration SaaS Applications ERP CRM
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64 AI Modern Data Warehousing - Emerging Trends
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65 CLAIRE ® : AI for Data Integration Expands Data Integration and Engineering – use cases, data, users AI-POWERED INTELLIGENCE & AUTOMATION NLP DRIVEN DATA PIPELINE GENERATION & OPERATIONALIZATION CLAIRE ® copilot NLP INTERFACE FOR DATA MANAGEMENT CLAIRE ® GPT Expected GA in Q1’25
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66 Data Integration for AI/Gen AI Analytics Apps Query Data Processing Optimized Storage Data Lake - S3 / ADLS / GCS Data Science Apps ELT Silver GoldBronze Data Formats & Patterns Real-time Events SaaS Semi- structured data Transactional Systems Cloud Data Integration SaaS Applications ERP CRM
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67 AI Apps Query Data & AI Processing AI Stores (LLMs and Vector db)Data Lake Data Science Apps Silver GoldBronze Data Formats & Patterns Real-time Events SaaS Semi- structured data Transactional Systems Analytics Apps Optimized Storage Cloud Data Integration ELT SaaS Applications ERP CRM Data Integration for AI/GenAI AI Engineering: ingestion, transformation, quality, retrieval, augmentation, generation, orchestration Unstructured data
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68 Organizations Are Focusing on Supporting AI with Quality and Trusted Data and Analytic Products Q. What are the top 3 strategic objectives for data leadership in the next 12–18 months for use of data and analytics in the organization? Very large companies have a higher priority on data quality and security. Digital natives are the most focused on supporting AI. Strategic objectives Source: IDC, “2024 Office of the CDO Survey Technical Perspectives: Data Challenges Influence Technology Investments and Outcomes,” Doc #US51070723, December 2024. (n = 848 total)
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69 Modern Data Architecture Data Warehouse Operational DB Lakehouse Data Lake Multidomain MDM Ingestion / Replication/ Virtualization Reverse ETL Ingestion / Replication/ Virtualization Reverse ETL API & App Integration | Process Automation AI-Powered | Unified Metadata | Orchestration | Elastic | Secure Other Data Tools BI & Analytics | Apps ML | Data Science Data Catalog Data Governance & Privacy Data Quality & Observability Data and AI Marketplace & Data Product API Consumption 360 Data Apps and AI | No Code LLM Orchestration DB Chunking Embedding Data Consumers | Predictive and Generative AI Models Data Sources ELT & ETL Vector
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70 Integrated Services on IDMC DATA CONSUMERS Data Engineer Citizen Integrator Data Scientist Data Analyst Business Users ETL Developer IoT Machine Data Logs + SaaS Apps Sources On- Premises Sources Real-time / Streaming Sources ApplicationsMainframe Databases + DATA SOURCES Connectivity Me tadat a System of Re cord AI-Powered M eta dat a Int elligence & Automat ion ® Intelligent Data Management Cloud DATA CATALOG MDM & 360 APPLICATIONS DATA MARKETPLACE API & APP INTEGRATION DATA INTEGRATION & ENGINEERING DATA QUALITY & OBSERVABILITY GOVERNANCE, ACCESS & PRIVACY
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71 Today’s Topics Submit Questions investors@informatica.com 1 Introduction Michael McLaughlin, Chief Financial Officer 2 Informatica Data Integration & Engineering in the Cloud • History of Data Warehousing • Informatica in the Modern Data Architecture • Hot Topics: Open Tables, Zero ETL, Data for AI Pratik Parekh, SVP, GM, Cloud Data Integration, Modernization and iPaaS 3 Informatica’s Role in Cloud Data Governance Brett Roscoe, SVP, GM, Governance and Cloud Ops 4 Wrap-up and Q&A ✓ ✓
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SVP, GM, Data Governance and Cloud Ops Informatica’s Role in Cloud Data Governance Brett Roscoe
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73 Data is Exploding and Diversifying • Enterprises typically have thousands of data sources • They typically span multiple ecosystems and CSPs
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74 Market View Diversity Increases Informatica CDO Insights Survey 2024 38% [of data leaders] grapple with an increasing volume and variety of data—41% already struggle with 1,000+ sources and 79% expect that number to increase in 2024
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75 Catalogs are Essential for Data Management • Ecosystem and CSPs have been announcing local or open -source catalogs • These catalog solutions target ecosystem data sources and use cases
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76 Comprehensive Coverage 1 Persona-Friendly Consumption 2 Fuel AI and Augmented Capabilities 3 Informatica Provides our Enterprise Customers with Unique and Durable Governance Value
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77 Informatica Provides our Enterprise Customers with Unique and Durable Governance Value Comprehensive Coverage 1 Vendor-independent, comprehensive view of your data estate
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78 Typical Vendor Specific Catalog Solution • There is some work to provide cross - ecosystem compatibility • This is primarily focused on moving data to the vendor’s ecosystem
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79 Typical Vendor Specific Catalog Solution • There continues to be a large amount of data outside the primary ecosystems • This can consist of large on -prem sources and custom sources not supported by all ecosystems
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80 Market View Diversity Increases Source: Gartner, Market Guide for Metadata Management Solutions, Mark Beyer, Guido De Simoni, 3 Sep 2024. GARTNER® is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. According to Gartner®: “Metadata capabilities embedded in the vast majority of existing data management solutions isolate their metadata usage and only allow access to their provider’s stack of solutions — perpetuating implementation silos that primarily defend revenue channels and increase user costs.”
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81 Comprehensive Governance and Catalog Solution • Informatica has broad support for diverse data across ecosystems and hybrid environments • Informatica uses advanced scanning to capture rich metadata • Knowing about the data is not enough INFORMATICA
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82 Market View Diversity Increases Source: IDC MarketScape: Worldwide Data Intelligence Platform Software 2024 Vendor Assessment, Doc #US51467224, Nov 2024 Decision-makers should evaluate data intelligence software platforms based on support of multicloud and hybrid environments, ensuring flexibility and scalability in data management and governance across diverse data ecosystems.
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83 Enrichment Comprehensive Coverage – “The Catalog of Catalogs” Ingest Data Sources - Cloud & On-Premises Business Intelligence Tooling Data Transformation (ETL, ELT, Replication) Business Intelligence Tooling 3rd Party Ecosystems & Hyperscalers / CSP *Representative list only Data Access Management Publish Data Marketplace & Data Product API Consumption with Data Protection Data Governance & Catalog Data Quality & Observability Security & Privacy Enforcement
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84 Informatica Provides our Enterprise Customers with Unique and Durable Governance Value Persona-Friendly Consumption 2 Integrated platform grounded in rich metadata enabling persona-friendly solutions
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85 Intelligent Data Management Cloud Data Governance & Catalog Understand and Govern Govern both data and AI models to help ensure trust Data Marketplace Share and Democratize Package data assets & AI models for consumption Data Quality & Observability Cleanse and Trust Identify, resolve anomalies & issues to grow data pipelines Data Access Management Protect and Comply Secure and share data responsibly for proper use
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86 ONE Metadata Repository Intelligent Data Management Cloud Data Governance & Catalog Understand and Govern Govern both data and AI models to help ensure trust Data Marketplace Share and Democratize Package data assets & AI models for consumption Data Quality & Observability Cleanse and Trust Identify, resolve anomalies & issues to grow data pipelines Data Access Management Protect and Comply Secure and share data responsibly for proper use
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87 What is Metadata? Data about data How is it being used How has it changed What type of data Sensitivity Importance of data Personal information Secure profile Data relationships Location/sovereignty Data tags/labels Classification Data quality assessments Business glossary alignment Data observability Who is responsible for it Data lineage
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88 Business Operations IT Strategy Finance Risk & Compliance Legal Research & Development Business Transformation Chief Data Office Marketing & Sales Data Stewardship Reporting & Analytics
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89 89 Sample Lineage for BI Report
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90 Persona-Friendly Consumption Data Steward | Data Analyst | Data Scientist Citizen Integrator | Data Engineer | ETL Developer Metadata Consumers Business Users | Data Analyst | Citizen Integrator Data Consumers Data Scientist | Data Engineer Developer Specialist Users Enrichment Data Access Management Publish Data Marketplace & Data Product API Consumption with Data Protection Data Governance & Catalog Data Quality & Observability Security & Privacy Enforcement
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91 Informatica Provides our Enterprise Customers with Unique and Durable Governance Value Fuel AI and Augmented Capabilities 3 AI architecture drives productivity, insight and value for the enterprise
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92 Fuel AI and Augmented Capabilities AI-POWERED INTELLIGENCE & AUTOMATION IN-PRODUCT AI-POWERED METADATA INTELLIGENCE AND AUTOMATION CLAIRE ® copilot NATURAL LANGUAGE INTERFACE TO DATA CLAIRE ® GPT Enrichment Data Access Management Publish Data Marketplace & Data Product API Consumption with Data Protection Data Governance & Catalog Data Quality & Observability Security & Privacy Enforcement
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93 Informatica Provides our Enterprise Customers with a Unique and Durable Governance and Catalog Value Vendor-independent, comprehensive view of your data estate 1 Integrated platform, grounded in rich metadata enabling persona-friendly solutions 2 AI architecture drives productivity, insight and value for the enterprise 3
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94 Security and Privacy Are the Top Challenges Faced by Organizations Today Q. What are the top 3 technical data challenges data leadership faces in the next 12–18 months? Organizations at the top of the scorecard face data distribution and AI challenges. Organizations at the bottom of the scorecard face data quality and AI challenges. Technical challenges Source: IDC, “2024 Office of the CDO Survey Technical Perspectives: Data Challenges Influence Technology Investments and Outcomes,” Doc #US51070723, December 2024. (n = 848 total)
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95 Integrated Services on IDMC to Scale Out Governance DATA CONSUMERS Data Engineer Citizen Integrator Data Scientist Data Analyst Business Users ETL Developer IoT Machine Data Logs + SaaS Apps Sources On- Premises Sources Real-time / Streaming Sources ApplicationsMainframe Databases + DATA SOURCES Connectivity Me tadat a System of Re cord AI-Powered M eta dat a Int elligen ce & Au tomat ion ® Intelligent Data Management Cloud DATA CATALOG MDM & 360 APPLICATIONS DATA MARKETPLACE API & APP INTEGRATION DATA INTEGRATION & ENGINEERING DATA QUALITY & OBSERVABILITY GOVERNANCE, ACCESS & PRIVACY
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96 Today’s Topics Submit Questions investors@informatica.com 1 Introduction Michael McLaughlin, Chief Financial Officer 2 Informatica Data Integration & Engineering in the Cloud • History of Data Warehousing • Informatica in the Modern Data Architecture • Hot Topics: Open Tables, Zero ETL, Data for AI Pratik Parekh, SVP, GM, Cloud Data Integration, Modernization and iPaaS 3 Informatica’s Role in Cloud Data Governance Brett Roscoe, SVP, GM, Governance and Cloud Ops 4 Wrap-up and Q&A ✓ ✓ ✓
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Chief Financial Officer Wrap-up and Q&A Michael McLaughlin
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98 The Big Picture Cloud Computing has Driven Massive Changes in Data Management: • Complexity has increased • Vendors and competitors have changed • Multicloud is the norm (and increasing) Enterprise grade data management from an independent, best-in-class vendor is every bit as essential in the modern cloud data world as it was in the on-premises era. Informatica's 'born-in-the-cloud' IDMC platform is uniquely able to serve the needs of Enterprise customers in the cloud, today and in the future.
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99 Informatica's Cloud Value-Add Cloud Data Integration Cloud Data Catalog & Governance Only Data Management Platform Best Data Management Products Multi-Vendor, Multi-Cloud & Hybrid
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100 Informatica is the Modern Cloud Data Solution Q3 '22 Q3 '23 Q3 '24 SQL ELT (Advanced Pushdown) Billions of Rows Processed Q3 '22 Q3 '23 Q3 '24 Cloud Change Data Capture Millions of Rows Processed Q3 '22 Q3 '23 Q3 '24 Cloud Data Integration Compute Hours Q3 '22 Q3 '23 Q3 '24 Cloud Data Catalog & Governance Millions of Records Note: Usage by IPU customers on IDMC; Usage volumes do not correspond directly to revenue from customers
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Q&A Submit Questions investors@informatica.com