and call. QX, year our Good to fourth Shane. welcome the high earnings exceeding with afternoon, XXXX guided end of quarter metrics. Thanks, all fiscal everyone, a closed We solid
percentage first Total quarter, to came million. revenue $XXX in Confluent operating time, million X.X%, and the $XXX first at our reached XX% for growing XX margin revenue improving XX%, Cloud points. grew non-GAAP positive
results the have platform category. streaming a Since than our rate These of our and operating testament going XX non-GAAP of more driven to growth power and percentage ago, public are X.X total the years improvement. we margin more doubled the than revenue run incredible points data in
for for adapting new be friction new on team to towards Last sales customers Cloud, maximize customers consumption-oriented shifting workloads driving go-to-market to the quarter, expansion. fully customers and model based a to in reduce compensation Confluent acquisition, transition potential pricing with and and and including orienting our product new landing logo landing accelerated consumption our field for and our incremental we discussed Cloud
revenue don't new the some we model change which internal already our a measures. said executed other of customer-facing rollout and new compensation metrics aspect, and all changes teams January effective initial of our to or and transformation, our the systems, model of model are As initial or changes business are go-to-market We've of consumption including X, before, any consumption-oriented. these
with Last I positive. marketing initial kickoff. and time from spent has very our our the teams sales week, The at team reaction been sales
We will in alongside of the these optimizing data business, streaming $XX front our few changes. and to fully platform believe transition spending be position puts leaders, our the next an business billion more us. category capture adapting to We a us opportunity fully in to excellent quarters, consumption-oriented our of
data storage streaming and its X These like way have of X increasing spend at a I'd handling potential recognition paths. thinking oriented the into technologies and data oriented them and handling minutes of is at to for a for systems areas growth. data as the very on data evolutionary those few and to One databases groups, those break rest, reflect about in motion. different category
database, a Over has at billion-plus infrastructure around the $XX concentrated powerful rest last highly the a data platform, category. decades, become several
by these reason use. of motion brokers, event technology its integration analysts products, fragmented remained landscape processing whether scale, including of and The of categories, ETL Each technology limits, categories highly latency, iPass cues, of product data tools, was integration data largely technological technologies more. or with and ease complexity tools, this application recognizing message The was technological. disparate for defined
and and platform handling scale similar nervous as all data motion. would be new supersedes importance platform Confluent's technologies streaming to The create data creation, the collapse these that motion potential that streaming platform of data of each for data system, in the of the the that thesis, central databases, our to data Since and a limited data is fragmentation precursors. been has a central
platforms data has Now stack. published research modern thesis category usage, component of by data to that validated a Forrester and the streaming has mission-critical this starting our distinct gotten get that recognition. to category December, In are it's scale that formal become
Wave QX Year a the Cloud Forrester streaming named streaming XXXX recognizes the and names Forrester leader technology were data an InfoWorld's of enterprise We as the management data Technology won leader. data XXXX category. QX The in a of platforms Confluent Pipelines Data streaming also Wave pulse the and in
a us these that era recognitions and here the is show data together, Taken streaming clear is Confluent leader.
the full before, to Kafka in the in the Organizations have, the platform streaming is this of time a real these they data just but motion. than and enterprise. data data is data govern across need the start just more discussed we've systems flows value process Kafka. extract of As to connect layer, it's data stream, foundational to
directly which capabilities, products, key on sizable lets of these of used processing friction. under with and connectors, a on governance own. same purchasing One aspect contract the path a drive Each these around consumption their consumption can go-to-market become additional stream is additional consumption business transformation is it to our be that no our
minutes in spend to end processing to the on batch at of as data than act a of waiting stream happening organizations like few covering the enables arrives it to I'd in the what's it Stream world process day. Today, rather processing.
information scaling streams and are these disruptions. minimize and streams pricing, processing, these But data that could throughout it flight logistics, be from the to information. powerful. times, and to streams enriched be customer cascade stream with By travel information drive itself, airline, an system weather can For of processing combined
this growth easier into as well spend applications on significantly represents significant these itself. and of adoption stream applications Confluent, platform a growth our Today, will be the our making By build believe stream both bringing of is we platform, than that to the opportunity. data our spend accelerated. as the business on For around higher the
address. of I'll spend reasons our few in to positioned Flink is are next X processing Confluent I'd stream addressing minutes succeed question the uniquely There to like the why key with offering.
de the emerging is Flink facto standard. First,
of the company third the stream the with rise products. gets Second, the is data processing. And
of address in Let these each turn. me
is results the attracted of community The from and the up presented processing. superiority was database obvious. addressing full fact have first developers processing technology space with special real-time is every the designed for of working the Flink designed streaming, to the is processing power aspect Flink stream most storage the needs tolerance with affects users. ground simply technological latency largest We from of design best This batch but believe the how interfaces as to the comes perhaps and apps. managed, the that This in of a the case works, of the reason was and failover has how
programming into one processing engines. than better or native to our as the batch supports result as well the popular And ability attracted most ecosystem have space. is and that platform this streaming offer features existing platform in This Kafka, real-time languages need complete choose it when databases for bolt like apps attempts technology spoken. community has streaming. batch they they in SQL doing The the development processing, that this most dramatically unifies real-time stream processing. and vibrant The is and to This developers is
proven has in This Capital Apple, significant bases, Flink having Apex downloads million developers. nearly taking a Perhaps Unlike support. technology. best users it itself of were like user broad has the of with One, increase Uber. including sophisticated XX% Flink commercial X and and truly there most job for one the open companies Netflix, without or attracted Flink best is unique this requisitions XXXX, the in what's go-to-market Kafka, is adoption Stripe backing most impressive engineers that In
or the winning position in in Flink leadership isn't us community. developer advantage our but technology the a technology gives investment to Our processing, stream and limited
As is the is stream data itself. the along it standard for agrees stream is, stream always attractive that doesn't Kafka stand alone. processing. of processing needs Everyone that as with It adopted a
are the prime the As we for emerging stream position in market. leaders capturing Kafka, in processing a
has is to this themselves is product, platform vastly data streaming product by together stream working processing storage made what towards with the simpler data customers Confluent unified Indeed, want. with processing We data unifying Database the into streaming very resulting same Kafka Flink. pairing believe a exactly a data is brought with driving databases the the via similar experience. successful.
processing is is semantics. the pairing coherent when Kafka. the offering Flink a be the performance discoverability work to any for layer single as from fastest data system, This easiest, Confluent just stream and going to that think data is not that Confluent's option a And is to make in the either. most processing we with to underlying transactional stream of and choice deep That default as it can security together skin comes optimized makes to obvious product unified layers a developer. processing kind
warehouse, largely products silo processed architecture. lived a a of reporting position and in up extracted processing, the In use data the data to trend architecture, classical clean to data that's and data at modern various where Confluent's that cases. stream There's supports single was for in most the role and destination, in make it and increasing final a it analytics was it usable data reusable
of Dozens for modern data. processing all from is architecture, up data publishing in of single completely is other use data upstream cleans hundreds destination source Repeating is even format The try having dozens a is source rather streams. times In ready-to-use that data. pulled That data is to infeasible. produce no to being the or critical data than the is Instead, destinations. clean for for the feed longer up the to responsible data systems, destination products. destination hundreds or the systems reusable warehouse the dozens that off high-quality, processing result the data the processed, of all to
enters batch the lakes. source. why, or into destination destination, at bulk in stream This processing stream processing of in is on stream won't we as in destinations the and happen processing this the warehouses systems databases, happening than data data from workloads data pulling means is like the This structurally, system is the rather the processing expect the
make into We powerful And reasons next-gen to draw platform. the the workloads. processing data workloads put enough think we'll streaming the these data are of together, nexus X DSP each
who organizations next see cases and to insights their predicting rich and unexpected can are generative demand These building applications, coding and wave to procurement like to GenAI trustworthy quickly cavities. of including the applications use contextually AI We Confluent their customers that to connect scale AI-powered detecting and chatbots, turn deliver systems continue customers. so LLMs build they proprietary software, platforms even to from
evolve represents term this a for opportunity the medium short in and Confluent long tremendous to production customers as in experimentation believe from We the term.
product we Anthropic, serverless in database Pinecone, their demands invest our and and to with partnered offering. ecosystem new Pinecone in address to partner Alongside vector continue our see a vendor, customers. recently across the We we
RAG proprietary their data real-time of build customers sources to bring augmented customers general-purpose state Our pipelines models. the that allow or integration with allows to generation retrieval AI together
improve their patterns. to poster into OpenAI us the child customer has signed become usage visibility In QX, GenAI. OpenAI of with
We are strategic and it, this AI still including continue across costs in This to validate like identified their their the early help reduce role have but use customer, already we to with generative landscape. additional customer cases, stages in streaming ways data of stack. others
that operates and the and ACERTUS Finally, It more manufacturers, I'd X our largest automotive United dealers car in vehicles. close move, like customer companies the title serves advantage. e-commerce underscore States. to and recondition, sold with dealers, register company finished platform rental to stories store, logistics
However, business systems customers were bottlenecks, pricing and supported that and the creating data siloed, chain old customers left duplicate delays, waiting. and records supply its
a to across business. its real-time to Cloud for data ACERTUS data to turnED access Confluent streaming platform provide So
and AWS, deliver instantly NetSuite, a data systems, applications them it in partners. warehouse. allow Stream data to external ACERTUS to processing in-flight process Connectors Snowflake and to connect Salesforce enables data and internal to including
internal platform, up for the and to find search as revenue topics new tens data the lines profit Stream the is With allows so new its customer tag can looking date Confluent ACERTUS savings and and and and of generate governance able anyone. for by data data to to delivering time millions been savings, margins. its know cost business trustworthy. streaming it's to increased So team they're accessible users of open has while serving
of By brand XX is growth. We growth in in users, strong native best new A the company great digital example. growing app fast-growing In segment. experienced millions matching to technology, in fashion a the to base XXXX, the months. while XXX% e-commerce particularly last by reached tens its world of we continue has India, the customer this see of massive
growth end-to-end Previously, power their real-time source with on and scaling and e-commerce in open relied large But data explosive to inventories management. maintenance over-provisioning. company's fulfillment, workflows, platform overheads Kafka, resulting order Kafka challenges open the came source
our open business they previously their used governance, full Kafka source to goals. to platform, So connectors ambitious turned with in to QX, stream and X-figure growth plans Cloud Confluent including a and leverage to power deal services that X support processing streaming
pleased XXXX. we're with finish year to fiscal In our strong closing,
ever that as and continued opportunity for more to are the fully of our platform, a market category-leading confident will serve billion transformation innovation We business than consumption-oriented our $XX us. in catalyst front in winning a
that, With turn Rohan. I'll things over to
