1、BOSTON CONSULTING GROUP 1BOSTON CONSULTING GROUP +RED HAT +STARBURST 1Few infrastructure technology markets have moved as quickly as data management,analytics,and artificial intelligence(AI).With data volumes growing exponential-ly and the data stack continuing to undergo rapid innovation in a varie

2、ty of areas from silicon to algorithms,companies are struggling to keep pace.Its a dilemma that executives must overcome not only because data and analytics are strategic imperatives but also because the associated costs are enormous and unsustainable.Spending on data-related software,services,and h

3、ardwarewhich already amounts to about half a trillion dollars globallyis expected to double in the next five years.This combination of complexity and costs is hurtling many companies toward a cliff that could cripple operations.To avoid falling off this precipice,most companies must adopt a fundamen

4、tally different approach to their architectures:a more federated and distributed paradigm.BCG research has determined that,in many cases,this approach will allow companies to solve the challenges of data access and integration in a siloed data landscape and accelerate innovation while maintaining th

5、e ability to lever-age legacy data stores.We are in the early days of this move-ment(which is known by names such as data products and data meshes)and are entering an exciting era in which new standards,market categories,and data management platforms will emerge.Proactive planning for federated arch

6、itectures is crucial for companies to stay ahead and fully capitalize on the potential of these new developments.Three Trends Reshaping the Data LandscapeA few key trends are driving profound changes in the data landscape.While many companies have been able to jerry-rig their data architectures over

7、 the years to accommodate new types of use cases,data sources,and tools,the new trends will soon overwhelm these efforts and demand a more comprehensive solution.A New Architecture to Manage Data Costs and Complexity By Pranay Ahlawat,Justin Borgman,Samuel Eden,Steven Huels,Jess Iandiorio,Amit Kumar

8、,and Philip Zakahi2 A NEW ARCHITECTURE TO MANAGE DATA COSTS AND COMPLEXITYTREND 1:THE VOLUME AND VELOCITY OF DATA ARE INCREASING The volume of data generated approximately doubled from 2018 to 2021 to about 84 ZB,a rate of growth that is expected to continue.We estimate that the volume of data gener

9、ated will rise at a compound annual growth rate(CAGR)of 21%from 2021 to 2024,reaching 149 ZB.Of all the new data generated,very little is actually stored.The percentage of stored data will nudge up from 6%in 2021 to 7%by 2024,with the storage of edge and cloud data projected to grow at CAGRs of 38%a

10、nd 40%,respectively,from 2021 to 2024.(See Exhibit 1.)About 95%of that 84 ZB of data is unstructured,including video streams,voice,and text,yet the storage rate of struc-tured data is growing faster than that of unstructured data as companies continue to expand business intelligence use cases,which

11、typically use more structured data.In addition,more than 50%of the data that some companies store is so-called dark data(meaning its not used in any manner to derive insights or for decision making),according to industry interviews.Managing this data poses an enormous chal-lenge but also a tremendou

12、s opportunity.TREND 2:DATA USE CASES ARE BECOMING BOTH MORE ACCESSIBLE AND MORE SPECIALIZEDHyperscalers(such as Amazon Web Services,Microsoft Azure,Google Cloud Platform)and open-source platform vendors(Red Hat,for example)have continued to make AI and data-driven application development more access

13、ible to developers and technical users.But the more exciting trend has been the growth of“citizen data scientists”and the empowerment of nontechnical users.Business users and teams are now more empowered than ever to make both strategic and buying decisions related to data.Business leaders(such as g

14、eneral managers and chief marketing officers)are using self-service reporting and analytics tools to unlock data-driven insights.For example,marketing teams can use auto machine-learning(ML)vendors like DataRobot to provide individualized data-driven customer experiences,and built-in AI tools such a

15、s Einstein Intelligence from Salesforce can help sales teams run advanced predictive analysis to accelerate sales and boost conversions.Volume of data stored(ZB)Projected 3-year CAGR(%)Edge138On-premise14Edge,structuredEdge,unstructured4437Cloud,structuredOn-premise,structuredOn-premise,unstructured

16、0543Cloud,unstructured372.9 ZB2024201865%18%13%1%60%5.1 ZB9%40%42%20214%4%2%1%5%6%7%4%9.8 ZB+20%CAGR+24%CAGRCloud4017%Sources:IDC Global DataSphere and StorageSphere,2022;BCGs Future of Data,2022,survey(N=299);BCG analysis.1Edge and on-premise structured and unstructured data combines IDC StorageSph

17、ere data with BCGs Future of Data survey data.Numbers have been rounded.Exhibit 1-Stored Data Volume Expected to Grow at 24%CAGR,Driven by Cloud and EdgeBOSTON CONSULTING GROUP 3BOSTON CONSULTING GROUP +RED HAT +STARBURST 3Accessibility will continue to increase as data literacy and a basic understa

18、nding of programming languages such as SQL become more widespread among nontechnical employ-ees.In a recent BCG survey,while only 45%of respondents stated that their company promotes data literacy among all employees,73%expect the number of nontechnical con-sumers of data will increase in the next t

19、hree years.Data,analytics,and AI-related use cases are also getting more sophisticated.Enterprise AI and ML began as basic ML techniques such as regression and clustering on struc-tured data to predict churn and segment customers,but the scope of problems and business value that AI and data can addr

20、ess and unlock have changed substantially in the past two to three years.Although its still early,advance-ments in deep learning,accelerator hardware,and the emergence of foundational AI models like BERT and OpenAI are redefining the art of the possible in language processing and generative AI(in ot

21、her words,AI that can generate novel content rather than simply analyzing or acting on existing data),such as conversational analytics,automated customer service,and content generation.But our research shows that the technology is evolving faster than some companies can adapt.Companies are still gra

22、ppling with legacy data sources and technology stacks,and often lack the talent to manage the massive business process changes required to fully utilize available use cases and unlock the data value proposition.According to a recent survey,only 54%of managers believe that their companys AI initiativ

23、es create tangible business value.TREND 3:TECHNOLOGY ADVANCEMENTS ARE SHIFTING DATA ECONOMICS Cloud not only has substantially increased the speed with which companies can adopt newer data technologies but also has shifted the economics.Usage-based,pay-as-you-go pricing models enable companies to sc

24、ale data usage along-side data growth,allowing them to pay for compute and ana-lytics only as they use it.Businesses are no longer bound by infrastructure investments or procurement timelines.At the same time,hyperscalers are continuing to shift the economics of data and AI by driving storage costs

25、down.(It helped that hardware costs per megabyte fell by more than 20%year-over-year from 2013 to 2021.)Declining cloud storage costs encourage companies to collect and store more data for consumption.Hyperscalers are also pushing the costs of compute and AI training down by developing custom silico

26、n(AWS Graviton and Google TPUs,for exam-ple).Indeed,according to our research,some customers lowered costs by 25%to 30%by moving to hyperscaler services and compute running on custom silicon.Beyond infrastructure,the software layer has progressed significantly.Storage and consumption-layer analytics

27、 are increasingly decoupled from one another,which gives customers the flexibility to apply analytics irrespective of the data storage format and location.Further,open source continues to advance the data layer.Open-source table and columnar formats such as Apache Iceberg,Parquet,and Arrow are accel

28、erating this trend.The influence of open source goes beyond just storage:it has funda-mentally changed the entire data stack,including data-base management(examples include Cassandra and MongoDB),database-processing engines(Presto,Trino,Spark,Hive),pipelines and integration(Airflow,Dbt),AI and analy

29、tics(PyTorch,Spark),and streaming(Kafka).Our research shows that the use of open source has grown by more than 13%year-over-year in the past decade(based on the number of open-source installations observed in large organizations),which further expands capabilities to leverage data,including dark dat

30、a and previously unretained data.Enterprise Architectures Stretched to the LimitsThese three trends are creating exciting new opportunities but also enormous challenges.Several internal and exter-nal issues are putting strain on todays architectures.Internally,most enterprises are struggling with th

31、e expo-nential data growth across multicloud and edge,adapting to new data and AI platforms,managing legacy data archi-tectures,and servicing increasingly complex use cases.Externally,the rise in data privacy regulations and a diffi-cult macroeconomic environment are creating pressure on IT spending

32、.Meanwhile,the perpetual shortage of data and AI talent is making it difficult to cope with these inter-nal and external challenges.In a BCG survey,more than 50%of data leaders said architectural complexity is a significant pain point.As a result,many companies find themselves at a tipping point,at

33、risk of drowning in a deluge of data,overburdened with complexity and costs.One big issue for companies is vendor proliferation across all data categories.According to PitchBook,US investment dollars for companies related to the data stack grew at 36%from 2012 to 2021,with investments totaling about

34、$245 billion during that period.But not all data categories are attracting the same attention from vendors.AI and ML,along with analytics,have seen the greatest number of new vendors,while vendor growth has been flat in other data categories,including relational databases,as the industry consolidate

35、s around a few commercial and open-source players.4 A NEW ARCHITECTURE TO MANAGE DATA COSTS AND COMPLEXITYA potentially more interesting trend is that several compa-nies are coming to market with a data-platform value proposition as they try to redefine traditional data market categories and cross b

36、oundaries.Here are just two exam-ples of vendors competing in more than one category:Ataccama started with data governance and has expanded into data integration and master data management(MDM),while Snowflake started as a cloud data warehouse and has expanded into analytics and broader data cloud.U

37、nfortunately,BCG research and interviews with custom-ers suggest that customers are struggling to understand these overlapping offerings,and the ever-evolving land-scape is contributing to market confusion.This vendor proliferation is driving stack fragmentation and technological complexity at compa

38、nies of all sizes,but these vary by maturity.Lower data maturity compa-nies typically use fewer vendors,have a centralized archi-tecture,and have few use cases.Larger companies with a more mature data stack experience more extreme stack fragmentation,often with multiple parallel data stacks servicin

39、g multiple use cases.At these companies,the total number of unique data vendors has nearly tripled in the past decadefrom about 50 to close to 150 today.(See Exhibit 2.)The fragmentation also varies by categories and submarkets.AI and business intelligence have seen the most vendor proliferation,whi

40、le more mature data catego-ries like relational databases have seen lesser proliferation with most enterprises standardizing around a few core commercial and open-source databases.And the number of vendors isnt the only problem another issue is the way companies use these vendors and evolve their ov

41、erall enterprise data architecture.Our research shows that as companies grow,different business units and teams build independent,often siloed data stacks to solve their specific needs,creating a brittle spider web of integration pipelines,data warehouses and lakes,and ML workflows.As companies move

42、 up the maturity curve,from data-driven to AI-driven organizations,the architectural complexity and fragmentation inevitably rise.Average number of unique vendors1 in the data stack of larger,more tech-forward companies2LakehouseData quality,catalog managementLakeETL and ELTMaster data managementNon

43、relational databasesStreamingWarehouseAnalytics processingAPIRelational databasesBusiness intelligence platforms,visualizationMachine-learning platforms050100150Sources:HG Insights;BCG analysis.1Long tail of vendors excluded;true vendor count may be higher.2Sample includes companies such as global t

44、ech firms,national health care companies,large financial services,top retailers,etc.(N=14,from a sample of 2,000+companies).ETL=extract,transform,load;ELT=extract,load,transform.Exhibit 2-Extreme Vendor Proliferation in the Data Stack of Large CompaniesAs companies move up the maturity curve,from da

45、ta-driven to AI-driven organizations,architectural complexity and fragmentation inevitably rise.6 A NEW ARCHITECTURE TO MANAGE DATA COSTS AND COMPLEXITYAlongside this surge in vendor complexity is double-digit growth in the total cost of ownership(TCO)of data,which we expect to double in the next fi

46、ve to seven years.This cost environment will have three key characteristics.First,we will continue to see a big shift from on-premise to cloud,while certain subcategories such as AI hardware will increase slightly.Our analysis suggests that on-premise software categories will stay relatively flat,wh

47、ile cloudnot surprisinglywill grow north of 25%year-over-year.Second,up to 80%of a companys data cloud spending will continue to be on usage-based compute resource costs(such as AI training and querying and analyzing data).So,while the total data stored on the cloud will go up,storage costs will not

48、 be a big driver of TCO growth.Third,people costs,which include third-party spending on system inte-grators and consulting firms,as well as internal data teams,will double in the next five years,driven by data complexity.(See Exhibit 3.)Despite the price and performance improvements in data,the grow

49、th in volume,the increased querying and analytics on that data,and the people investments needed outpace the efficiency gains.In a BCG survey,56%of managers said managing data-operating costs is a pain point,but they are continuing to boost their investments in modernizing and building new data arch

50、itectures.In other words,the bene-fits outweigh the painfor now.But these cost increases have been regularly outpacing IT budget growth,and data operation costs could come under intense pressure in a recessionary environment or period of belt-tightening.Just like in the past,we expect economics to i

51、nfluence how enterprise data architectures evolve(shifting away from capital expenditure with cloud,for example).To manage the costs of a modern data architecture,several short-term,tactical options are popular,including deduplication,re-stricting use,and tiered storage and analytics(such as using c

52、heaper cold-storage options for less critical data instead of always using a data warehouse).In the longer term,however,a fundamentally different approach is need-ed to manage the spiraling complexity and to scale the architecture more effectively.Cost by data life cycleCost by type and locationProj

53、ected 3-yearforward CAGR(%)62Data storesand queryingData movementand ETL and ELTSecurity andgovernance2025ETotal:13Data generationand staging524330100 2145 56 2022 52025ECloud(storage)Cloud(compute)Hardware6On-premise software0People12Services16Analytics a

54、ndconsumption20Projected 3-yearforward CAGR(%)Total:13272436Source:BCG analysis.Note:2022 cost contribution indexed to 100.ETL=extract,transform,load;ELT=extract,load,transform.Exhibit 3-Costs Shifting Toward Analytics and CloudCompanies need to be pragmatic.A meshed or service-oriented data archite

55、cture is not a panacea or silver bullet.8 A NEW ARCHITECTURE TO MANAGE DATA COSTS AND COMPLEXITYLessons for a New Data Architecture Given the rapid growth of data and use-case volume,in-creasing architectural complexity,and rising costs of data,more companies are reaching a breaking point.Tactical f

56、ixes will no longer suffice.Whats needed is a data architecture that provides flexibility for the future but is built with todays requirements and realities in mind.For companies willing to take this on,we have codified three key lessons.LESSON 1:ARCHITECTURE WILL BECOME MORE DECOUPLED,FEDERATED,AND

57、 SERVICE-ORIENTEDThe underlying scalability and efficacy of an enterprise data architecture depends on two related functions:trans-ferring data between applications and across clouds in a systematized and real-time manner;and making the pro-duction and consumption of data for AI and analytics easier

58、.To overcome the current challenges,companies must adopt a more federated and distributed architecture paradigm.(See Exhibit 4.)This is akin to moving to a more service-oriented or micro-services-based architecture in software.This setup will allow organizations to share data more easily;it will als

59、o facilitate the interaction between data services and data products through well-architected APIs.There are many names for this architecture setup(including data mesh or data products),but the core underlying principle is to apply abstraction and service orientation to data.According to our 2022 Fu

60、ture of Data survey,68%of data leaders aspire to implement such an architecture in the next three years.In this new model,domain experts can curate their data products and,if necessary,provide other domains access to the data in a secure manner.Data stack fragmentation remains,but because the comple

61、xity is hidden behind a service,the company can decouple the underlying architec-tures and use different substacks without inhibiting data usage.Moreover,an organization does not need to have a single architecture design.Companies can build some data products and services on traditional warehouses a

62、nd others on data lakes to optimize for business needs.Fragmented or bespoke(10%30%of all enterprises)Less mature enterprisesMore mature enterprisesConsumption layerData sources,databases,object stores,etc.Centralized(60%80%of all enterprises)Federated or service-oriented(5%of all enterprises)Consum

63、ption layerCentral data store(e.g.,warehouse,lake,lakehouse)Data sources,databases,object stores,etc.Consumption layerSimple architectureNot scalable,prone to inefficiencies such asdata duplication and data lineage issuesScalable,highly performant,easier to managea single source of truthMore expensi

64、ve,reduces enterprise agility,requires more data movementFlexible and extensible,increased domainspecialization,more cost efficient,lessunnecessary data movement,reducedlatency,stronger security if managedappropriatelyDifficult to execute and complex,high skill requirements,low tooling maturityABCAB

65、CData productteam BABCData productteam AABCAbstraction layerSource:BCG analysis.Note:Enterprises usually use a combination of different architectures.Exhibit 4-Architectures Are Evolving to Be More Federated and Service-OrientedBOSTON CONSULTING GROUP 9BOSTON CONSULTING GROUP +RED HAT +STARBURST 9Th

66、is new paradigm obviously has implications for how data is managed.Data movement and data duplication will be minimized.Because individual services can control access and adopt a zero-trust posture,they can more easily han-dle data lineage and security issues,thus lessening the wholesale movement of

67、 data.Importantly,product thinking will underpin how companies build data services,and data products will be viewed through a value lens(data ROI)with a focus on the end user.Another advantage of this federated approach is that compa-nies can leverage existing infrastructure investments for new use

68、cases and upgrade and update individual data products as needed.Different teams also have the freedom to pick the right tools for the right job.One team might use an in-memory columnar database for low-latency reads,while another might use a data lake built on low-cost storage.Companies need to be p

69、ragmatic,however.A meshed or service-oriented data architecture is not a panacea or silver bullet.Businesses should always evaluate their architec-ture on a source-by-source and use-case-by-use-case basis rather than trying to use the same tool for every problem.For simpler use cases,such as dashboa

70、rding,a centralized architecture might suffice and be more suitable.LESSON 2:NEW STANDARDS,PROTOCOLS,AND MARKET CATEGORIES WILL EMERGE We are in the very early innings of this shift to a new data architecture,and there are no openly defined standards or protocols on how these services are defined or

71、 talk to one another.The industry must define standards and tools for data transfer formats,service definitions,service discovery,and registry(among others).For example,new standards similar to XML,JNDI,REST,gRPC,and SOAP must emerge so that different data services can communicate.Lessons from the e

72、volution of software architecture are instructive.Early adopters and trailblazing companies such as Google and Netflix established patterns for DevOps and microservices(leading to community projects like Kubernetes and Spinnaker).We expect the same evolutionary arc in data.New open-source projects,c

73、ommunity-driven standards,and commercial tooling will emerge as more companies adopt distributed services,data products,and data mesh architectures.As tools improve,best-practice patterns will emerge and the barri-ers to adopting this approach will continue to fall.With this in mind,data vendors nee

74、d to move beyond data management and analytics and start developing many new tools,such as:Middleware to help with data format conversion,data production,and consumption Tooling for data versioning and time travel for data,which is akin to source control management in software Next-generation data o

75、bservability,operations,and MLOps platforms for service-oriented architectures A new paradigm of ETL tools with data automation and trigger mechanisms to automatically link different data services,train,and deploy new AI models Platforms to compose,introspect,discover,and govern data products and se

76、rvices New types of identity access and identity governance tools to secure data access LESSON 3:OPEN SOURCE AND HYPERSCALERS WILL CONTINUE TO INFLUENCE TECHNOLOGY CHOICES The need to manage spiraling costs will drive many enter-prise data architecture choices.On the software side of data management

77、,open source will continue to be critical.Our research indicates that multiple dynamics have driven the growth of open source:the emergence of commercial open source as a compelling business model,Big Tech and hyperscalers throwing their weight behind open source,and the power of communi-ty-driven d

78、evelopment and emergence of multiple founda-tions including Apache,the Cloud Native Computing Foun-dation(CNCF),and the Linux Foundation.Beyond these drivers,open source decreases the total costs of the data stack.Our research shows a cost reduction of 15%to 40%for some customers.On the hardware and

79、 infrastructure side,hyperscalers keep pushing the boundaries of price and performance by con-tinuing to drop prices on storage as well as creating server-less and pay-as-you-go data services(Aurora Serverless and investing in custom silicon,for example).Cloud is becom-ing the center of gravity for

80、data and analytics.Indeed,multiple organizations have already embraced cloud as the primary location for their data-intensive workloads and applications.At the same time,four out of five enterprise customers have adopted a multicloud posture and are building enterprise architectures to avoid vendor

81、lock-in while still being able to use innovative cloud services as they emerge.10 A NEW ARCHITECTURE TO MANAGE DATA COSTS AND COMPLEXITYKey TakeawaysOn the basis of the broad trends shaping the data land-scape and major lessons for designing a new enterprise data architecture,we have identified some

82、 key takeaways for enterprises and vendors.ENTERPRISES Key takeaway 1:Pay close attention to overall data TCO.To keep costs under control,baseline and deaverage spending to understand key driverssuch as people,data transfer and movement,data storage,and software.Drive shorter-term tactical cost impr

83、ovements by exploring multiple approaches.First,purge and kill data initiatives that are not yielding value.Second,consolidate vendors where possible.Third,improve data infrastructure utilization by deduplicating data and opti-mizing cloud costs.Key takeaway 2:Make strategic investments in service-o

84、riented data architectures,adapt quickly,and remain agile.Implement pilots to experiment with federated data architectures,and test multiple vendors and technologies to assess technical viability.This will help build critical internal skills and position companies to move fast.Because federated arch

85、itectures are not a panacea or one-size-fits-all solution,run these pilots pragmatically and with an open mind.Be prepared to change.The evolution to a federated architecture might take time,and standards will evolve rapidly.Key takeaway 3:Continue to invest in talent.Invest in training and upskilli

86、ng the existing workforce and hiring new staff to strengthen the talent pool.When this is not possible,explore partnerships with consulting firms and systems integrators to bridge the talent gap in the near term.SOFTWARE AND DATA VENDORS Key takeaway 1:Stay alert for new data market categories,compe

87、tition,and tools.This market will see rapid evolution and the creation of new categories and submarkets.Revisit strategy and pay close atten-tion to new community projects,along with competitive moves from data management companies and hyper-scalers.Be prepared to adapt product roadmaps and reevalua

88、te value propositions to capitalize on this mega trend.Key takeaway 2:Participate in establishing new standards.This new data market will be founded on open source and open standards,so position yourself as an influencer of these new standards.Sponsoring indus-try consortiums,having a seat at the ta

89、ble,and engaging the community early are strategic imperatives.Key takeaway 3:Meet customers where they are and help them with change management.To drive adoption,its important to understand your customers.First,deaverage customer segments.Different custom-ers are at different places in the maturity

90、 arc.In the near term,go after the early adopters and customers with more data stack fragmentation.Second,concentrate on customer education and consultative selling to cut through the market and vendor noise.Third,focus on customer needs post-sales by helping them scale your platforms,and partner wi

91、th system integrators and consulting firms.The adage that“the only constant is change”applies perfectly to the evolution of the data market.The pace of innovation,however,has overwhelmed enterprises that are struggling to keep up with the complexity of their data stack and manage the costs.To fully

92、unlock the data value proposition,companies must take a page from the software architecture playbook and start building more decoupled,service-oriented data architectures.We are in the very early stages of this exciting architecture revolution,which will create new standards,vendors,and market categ

93、ories.For software companies and other enterprises,the ability to adapt quickly,more than anything,will determine the winners of tomorrow.BOSTON CONSULTING GROUP 11BOSTON CONSULTING GROUP +RED HAT +STARBURST 11About the AuthorsPranay Ahlawat is a partner and associate director in the Washington,DC,o

94、ffice of Boston Consulting Group,focused on enterprise software and cloud.You may contact him at .Justin Borgman is the co-founder and CEO of Starburst.You may contact him by email at justinstarburst.io.Samuel Eden is a project leader in BCGs San Francisco office.You may contact him by email at .Ste

95、ven Huels is a senior director in Red Hats Cloud Services business unit responsible for AI and machine learning.You may contact him by email at .Jess Iandiorio is the CMO of Starburst.You may contact her by email at jess.iandioriostarburst.io.Amit Kumar is a managing director and partner in BCGs Bos

96、ton office.You may contact him by email at .Philip Zakahi is a managing director and partner in the firms New York office.You may contact him by email at .For Further ContactIf you would like to discuss this report,please contact the authors.AcknowledgmentsThe authors thank the following for their c

97、ontributions to the development of this report:Tatu Heikkil,Sesh Iyer,Derek Kennedy,Jill Roberts,Sherry Ruan,Omar Shaat,Vikas Taneja,and David Wang.Boston Consulting GroupBoston Consulting Group partners with leaders in business and society to tackle their most important challenges and capture their

98、 greatest opportunities.BCG was the pioneer in business strategy when it was founded in 1963.Today,we work closely with clients to embrace a transformational approach aimed at benefiting all stakeholdersempowering organizations to grow,build sustainable competitive advantage,and drive positive socie

99、tal impact.Our diverse,global teams bring deep industry and functional expertise and a range of perspectives that question the status quo and spark change.BCG delivers solutions through leading-edge management consulting,technology and design,and corporate and digital ventures.We work in a uniquely

100、collaborative model across the firm and throughout all levels of the client organization,fueled by the goal of helping our clients thrive and enabling them to make the world a better place.Red HatRed Hat is the worlds leading provider of enterprise open source solutionsincluding Linux,cloud,containe

101、r,and Kubernetes.We deliver hardened solutions that make it easier for enterprises to work across platforms and environments,from the core datacenter to the network edge.StarburstStarburst is the analytics engine for distributed data.They provide the fastest,most efficient analytics engine for your

102、data warehouse,data lake,or data mesh.They unlock the value of distributed data by making it fast and easy to access,no matter where it lives.Starburst queries data across any database,making it instantly actionable for data-driven organizations.With Starburst,teams can lower the total cost of their

103、 infrastructure and analytics investments,prevent vendor lock-in,and use the existing tools that work for their business.Trusted by companies like Apache Corporation,Comcast,Doordash,FINRA,and VMware,Starburst helps companies make better decisions faster on all data.Boston Consulting Group 2023.All rights reserved.2/23 For information or permission to reprint,please contact BCG at .To find the latest BCG content and register to receive e-alerts on this topic or others,please visit .Follow Boston Consulting Group on Facebook and Twitter.