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  • 10 Best Data Science Recruiters for Top Industry Placements in 2024 Yeeply

    10 Best Data Science Recruiters for Top Industry Placements in 2024 Yeeply


    Finding the right talent in data science is crucial for your company’s success. Navigating through numerous recruitment agencies and headhunters can be overwhelming. We have identified top data science recruiters to streamline this process for you.

    These professionals specialize in matching skilled data scientists with employers. With evolving technology and increasing demand, it’s essential to choose the right headhunter data science to fulfill your needs. With our guide, your search for top-tier talent will become much simpler.

    data science recruiters representation

    1. Yeeply

    Yeeply specializes in matching top tech talent with companies. We have a refined process to ensure the right fit for your team. Our agile, flexible approach makes them a standout choice for hiring in the tech sector.

    If you’re looking for a headhunter data science expert, Yeeply is a strong option. We offer comprehensive services to find the best candidates.

    Yeeply helps you hire the best digital and remote tech talent. This includes professionals in data science, ensuring you get skilled and capable individuals for your projects.

    Moreover, our network includes a wide range of specialists, making it easier to find the right match. This is particularly useful for companies seeking to build strong data science teams.

    Our platform is user-friendly, allowing you to streamline the hiring process. You can quickly connect with potential hires and manage communications effectively.

    Choosing Yeeply can save your company time and resources. Our focus on quality and efficiency helps you find top talent without the typical hassles of recruitment.

    2. Insight Global

    Insight Global has a vast database of skilled data scientists ready to join your company. They ensure a smooth recruitment process from start to finish.

    You will receive help in building a pool of qualified applicants. Insight Global screens these candidates thoroughly, conducts interviews, and aids in making the final hire.

    Working with Insight Global means you’re not just getting a recruiter, but a partner in finding top talent. They have extensive experience as a headhunter in data science.

    Companies in various industries trust Insight Global for their data science needs. They make sure to match the right talent with the right opportunities.

    If you need a reliable headhunter in data science, consider reaching out to Insight Global. They are committed to helping your company thrive in today’s data-driven world.

    3. Harnham

    Harnham specializes in recruiting within the data science sector. They are known for their consultative approach that aims to provide value to both clients and candidates. This focus makes them a strong choice when you are looking for a data science recruitment partner.

    Founded in 2006, Harnham has gained a reputation for building market-leading data science teams. They operate globally, with a strong presence in Europe and the UK. This makes them an ideal option for companies in these regions.

    Their communication is consistently praised by clients. The feedback suggests that Harnham’s team is both professional and effective. They are considered one of the best data science recruiters around.

    For those interested in finding a headhunter data science firm, Harnham stands out. Their ability to match top talent with the right opportunities has made them a preferred choice among many employers. You can read more about Harnham on their official website.

    Candidates seeking roles in data science will also find Harnham to be a valuable resource. Their job listings include positions in various locations and specializations, such as AI and machine learning. This ensures a good match for different career aspirations in the data science field.

    4. Michael Page

    Michael Page is renowned for its expertise in information technology recruitment. With a strong focus on tech talent, this firm excels at finding top candidates for both permanent and contract roles. Their approach is driven by data, utilizing a vast exclusive database and high-quality networks.

    The company’s deep understanding of the tech industry ensures they can meet tight timelines without compromising quality. Michael Page’s team of recruiters, who also serve as headhunter data science experts, leverage data-driven insights to match the best candidates with the right opportunities.

    When you are searching for data science professionals, Michael Page stands out. They have the resources and the industry knowledge to help you find the talent you need. You can trust their skilled recruiters to navigate the complex hiring landscape, ensuring you get the best fit for your team.

    Their commitment to protecting candidates from recruitment scams adds an extra layer of trust. Michael Page makes sure candidates feel secure during the process, providing reliable and professional services. This dedication sets them apart in the competitive world of data science recruitment.

    For a dependable partner in your hiring journey, Michael Page’s headhunter data science expertise offers what you need. Their specialized services and knowledgeable team make the recruitment process smoother and more efficient. When you need the best in IT and data science, Michael Page is a top choice.

    5. Robert Half

    Robert Half is a well-known staffing agency that excels in data science recruitment. They have a strong reputation for connecting top talent with leading companies. If you’re looking for a reliable partner to help fill data science roles, their expertise can be invaluable.

    The company provides comprehensive services and has specialized recruiters who understand the complexities of data science. They can quickly identify the right candidates for your needs. This makes them a strong choice for both job seekers and employers.

    One key advantage of Robert Half is their broad reach. They operate in multiple countries, giving you access to a wide pool of talent. This international presence can be a significant benefit when looking for specialized skills in data science.

    Not only does Robert Half offer traditional staffing solutions, but they also have expertise as a headhunter for data science roles. This means they can find highly specialized and high-level candidates that other agencies might miss.

    Consider Robert Half if you need help with either short-term staffing or long-term hires. Their deep industry knowledge and global reach make them a valuable partner in data science recruitment.

    6. TEKsystems

    TEKsystems is a leader in IT recruiting and offers specialized services for data science roles. Their team connects top-tier talent with companies needing expertise in data analytics and artificial intelligence.

    As a headhunter data science specialist, TEKsystems has a strong track record of placing skilled professionals in rewarding positions. They work closely with businesses to understand their needs and find the right match.

    You will find that TEKsystems supports your career growth and aims to align your skills with cutting-edge opportunities. Their national recruiters are dedicated to making meaningful connections in the data science field.

    Whether you’re a company looking for talent or a professional seeking new opportunities, TEKsystems can be a valuable partner. Their expertise in the IT industry helps ensure you find the right fit.

    Exploring options with a headhunter data science recruiter like TEKsystems can open doors to exciting and innovative roles within top organizations.

    7. CyberCoders

    CyberCoders is a well-known name in the field of data science recruitment. They stand out for their innovative technology and reliable recruiters.

    If you are looking for experienced headhunter data science experts, CyberCoders offers a wide range of opportunities.

    With CyberCoders, you can expect a streamlined process for job matching. They focus on placing professionals in various sectors such as tech startups, healthcare, and life sciences.

    As a headhunter data science platform, CyberCoders places a strong emphasis on both speed and accuracy. Whether you are a company in need of top data science talent or a professional seeking new job opportunities, CyberCoders can help you achieve your goals.

    Explore more about them on their website.

    8. Kforce

    Kforce stands out as a leading recruiter in the data science field. Recognized by Forbes as one of America’s Best Recruiting Firms, Kforce is known for connecting top talent with the right opportunities.

    Kforce has built a strong reputation for understanding both the needs of companies and the skills of data science professionals. If you are looking for a headhunter data science recruiter, Kforce’s network and expertise can be invaluable.

    You can trust that Kforce’s services are backed by extensive experience and deep industry knowledge. As a headhunter data science recruiter, Kforce’s team is skilled at matching qualified candidates with the right job roles, ensuring a good fit for both parties.

    Their proven track record and positive client feedback make Kforce a go-to choice for anyone in the data science industry. Whether you are a company seeking new talent or a professional looking to advance your career, Kforce offers reliable recruitment services.

    9. Nigel Frank

    Nigel Frank is a global leader in Microsoft technology recruitment. Specializing in Dynamics, Azure, and other Microsoft jobs, they offer a wide range of opportunities tailored to your skills and experience.

    As a headhunter for data science roles, Nigel Frank provides access to many Data Engineering positions. Their dedicated consultants work closely with you to find the best fit.

    With their deep industry knowledge, Nigel Frank ensures that you receive personalized service. This makes it easier to break into data science or advance your career. You can explore available positions on their official website.

    Nigel Frank regularly updates its job listings, ensuring that you have access to the latest openings. Whether you are starting out or looking to advance, they can help you find your next role.

    If you are interested in positions involving Microsoft technologies and data science, Nigel Frank is a strong choice. Their extensive network and expertise as a headhunter for data science roles can help you land your dream job.

    10. Randstad

    Randstad is well-known for its extensive network and experience in staffing. As one of the leading firms in the industry, they have a robust process for finding the right talent. Their focus on data science roles is a key part of their services.

    You can expect Randstad to leverage their expansive contacts and deep knowledge of the market. They match candidates to positions that fit their skills closely. This precision ensures both companies and job seekers are satisfied.

    As a headhunter data science, Randstad offers roles in various sectors. From tech startups to established enterprises, their reach covers a broad spectrum. This diversity increases your chances of finding a suitable position.

    The company’s approach is not just about filling vacancies. They aim to build long-term relationships with both clients and candidates. This strategy leads to better placements and more stability in employment.

    Working with Randstad means you benefit from their industry insights. Their guidance can help you navigate the competitive data science job market. For anyone looking to advance their career, partnering with a headhunter data science like Randstad can be a strategic move.

    To learn more about their offerings, visit Randstad USA.

    Close-up Photo of Gray Laptop

    The Role of Data Science Recruiters

    Data science recruiters play a crucial part in connecting the right talent with companies in need of data expertise. These professionals ensure that businesses can harness data effectively and maintain a competitive edge.

    Importance in the Industry

    Data science recruiters are vital in today’s tech-driven world. They help bridge the gap between skilled data professionals and organizations. Businesses require experts in fields like machine learning, data modeling, and data analytics to thrive. Without these experts, companies could struggle to make data-driven decisions.

    Recruiters focus on finding candidates who not only possess technical skills but can also innovate and provide strategic insights. Their role is to ensure that the talent they hire can impact the overall growth and efficiency of the company. This aspect makes the job of data science recruiters highly significant.

    Key Responsibilities

    Identifying Top Talent: Headhunter data science professionals search for the best candidates with expertise in areas like machine learning, AI, and data analytics. They use strategic talent sourcing to find individuals who meet specific needs.

    Screening and Evaluation: Recruiters are responsible for evaluating candidates‘ technical skills and their ability to fit into an organization’s culture. This includes conducting interviews, assessing technical tests, and checking references.

    Collaboration with Companies: They work closely with businesses to understand their specific requirements. This includes working with hiring managers to determine the skills and experience needed for each role.

    Facilitating the Hiring Process: Headhunter data science recruiters streamline the hiring process by coordinating interviews, negotiating job offers, and ensuring a smooth onboarding process. This helps both candidates and employers save time and resources.

    Ongoing Relationship Building: Maintaining relationships with both clients and candidates is key. This ensures long-term satisfaction and can lead to future placements, making this an ongoing cycle of matching talent with opportunities.

    How to Choose the Right Data Science Recruiter

    Choosing the right recruiter is crucial for finding top-notch data science talent. Focus on a firm’s experience and use key questions to ensure they meet your needs.

    Evaluating Experience and Expertise

    Look at the recruiter’s background in the data science industry. Check how long they’ve been placing data scientists and if they specialize in roles such as machine learning, NLP, or AI. A recruiter like Averity in New York City has a proven track record in these areas.

    Ask about their past clients. Have they worked with tech startups or large corporations? Recruiters like Redfish Technology have experience across various sectors like healthcare and life sciences.

    Also, find out if they offer specialized services such as headhunter data science roles. For example, firms like Alpha Apex Group excel in strategic talent sourcing.

    Questions to Ask Prospective Recruiters

    Before making a decision, prepare a list of questions for your potential recruiter. Start with inquiries about their process. How do they identify and screen top data science talent?

    Get specific. Ask how they stay updated with industry trends and the latest technologies. A good recruiter should have a solid grasp of emerging fields like deep learning and advanced analytics.

    Lastly, clarify their terms and fees. Do they charge a flat fee or a percentage of the hired candidate’s salary? Understanding this will help you budget accordingly and avoid unexpected costs.

    By asking these questions, you can better assess if a recruiter specializes in the complex field of data science and if they can match you with the right candidates.

    Frequently Asked Questions

    Finding the right data science recruiter or headhunter can make a significant impact on your career. It’s important to know how to identify specialized agencies and understand the key criteria for choosing the best one for your needs.

    How can I identify specialized recruitment agencies for data science positions?

    Look for agencies with a strong focus on tech or data science, such as Insight Global. They should have a proven track record of placing candidates in data science roles. You can also request case studies or success stories from the recruiter.

    What criteria should I consider when choosing a recruiter for data science roles?

    Focus on the recruiter’s experience and expertise in the data science field. Evaluate their network and connections within the industry. Agencies like Harnham are known for their specialization in analytics recruitment. Communication and transparency are also key factors.

    Which firms are known for high-quality data science executive searches?

    Firms like Yeeply are well-regarded for executive searches in data science. Other top firms include Harnham and Robert Half, which have dedicated teams for high-level placements.

    What are the best strategies for connecting with recruiters in the data science industry?

    Attend industry conferences and networking events to meet recruiters. Utilize LinkedIn to connect with them and showcase your skills. Engaging with online forums and groups focused on data science can also lead you to top headhunter data science professionals.

    How does the demand for data scientists vary by geographic location?

    The demand for data scientists is higher in tech hubs like San Francisco, New York, and Seattle. However, cities like Austin and Boston are also gaining traction. The demand is generally higher in areas with a significant presence of tech firms and startups.

    Which companies are recognized for paying premium salaries to data scientists?

    Tech giants like Google, Facebook, and Amazon are known for offering top salaries to data scientists. Companies in the finance and healthcare sectors also tend to pay premium wages due to the critical nature of data analysis in their operations.

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  • Alchemer’s commitment to data quality

    Alchemer’s commitment to data quality


    Recent news of a $10 million survey fraud scheme is sending shockwaves through the market research industry. As investigators uncover the extent of this nearly decade-long scheme, one thing is clear: the integrity of survey data cannot be taken for granted. 

    As a trusted market research provider, this story is a powerful reminder of why Alchemer’s commitment to ethical research and uncompromising data quality is non-negotiable.  

    Great insights start with quality data  

    To put it simply, insights that come from bad data lead to bad decisions. Inaccurate or fraudulent survey responses skew results, erode customer trust, and derail entire programs and strategies.  

    The spread of AI-powered bots and sophisticated fraudsters poses a significant threat to the integrity of market research studies and the industry as a whole. Survey fraud isn’t just a technical hurdle to overcome—it’s an ethical challenge that impacts researchers, organizations, and the people they aim to serve. 

    This is why Alchemer’s Research Solutions Team combines advanced technology with human oversight to detect and eliminate fraudulent responses before they compromise a research study.  

    Alchemer’s approach to fraud prevention  

    From device fingerprinting and in-survey behavioral analysis to manual response audits, Alchemer takes a multi-step approach to data quality and fraud prevention. With rigorous processes and standards, Alchemer ends up scrubbing 10–15% more fraudulent data than other providers, ensuring the insights you receive are complete, legitimate, and human.  

    Step 1: Pre-survey forensic screening 

    Before a respondent ever sees a question, they are screened using real-time device-level fingerprinting and forensic analysis. This initial checkpoint evaluates more than 40 unique markers and assigns a fraud score based on the likelihood and type of fraudulent behavior detected. Helping to identify and block: 

    • Bots and automated behavior 
    • Emulators and simulators 
    • Residential proxies and data center IPs 
    • Dark web-originated traffic 
    • Location and browser spoofing 
    • Device/account duplication 
    • Device oscillation and velocity anomalies 

    Step 2: Post-survey human and algorithmic review 

    After a respondent completes a survey, there is an additional step to ensure data accuracy. Using advanced technology and human expertise, the Research Solutions Team reviews open-ended answers for unnatural language patterns, odd phrasing, or copy/paste behavior, and flag responses that move too quickly or don’t follow logical patterns. 

    This extra layer of review helps identify: 

    • Contradictory or nonsensical answers 
    • Pre-written or scripted responses 
    • Unusually fast completion times 
    • Responses that don’t reflect real human input 

    Preventing survey fraud together 

    Maintaining data integrity is a shared responsibility and that responsibility starts with awareness and vigilance. As partners in the research process, the Research Solutions Team is committed to helping you uphold the highest standards of data quality.  

    In every client engagement the Reseach Solutions Team recommends the following best practices:  

    1. Stay informed: Keep up with evolving best practices in survey design and fraud prevention. From smart screening techniques to subtle shifts in question phrasing, even small adjustments make a big impact in deterring fraud and protecting the reliability of your data. 
       
    1. Embed quality checks throughout: Build data validation, logic traps, and timing checks into your surveys from the start. This proactive approach allows you to identify potential issues in real time and significantly reduces the risk of bad data slipping through the cracks. 
       
    1. Be vigilant: Pay close attention to red flags, such as inconsistent answers, patterned responses, or unusually fast completions. These warning signs may indicate fraudulent behavior and should prompt a deeper review. 
       
    1. Collaborate with the Research Solutions Team: When something seems off, they are there to help. Our team of experts can assist in identifying potential anomalies, adjusting your survey setup, and reinforcing your fraud prevention strategies.  

    Confidence with every decision  

    At Alchemer, we will continue to prioritize data quality in everything we do, because we understand that without good data, even the most sophisticated study loses its value.  

    Trust in research begins with trust in data, and that’s a responsibility we take seriously every day. 

    For more information about our data quality practices or to discuss your current or future research projects, please contact the Research Solutions Team today. 



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  • Data Structures & Algorithms in Swift

    Data Structures & Algorithms in Swift


    Understanding how data structures and algorithms work in code is crucial for creating efficient and scalable apps and acing job interviews. Swift’s standard library and, more recently, the Swift Collections and Algorithms packages contain a robust set of general-purpose collection types and algorithms, yet they don’t cover every case!

    In Data Structures and Algorithms in Swift, you’ll learn how to implement the most popular and useful data structures and when and why you should use one particular data structure or algorithm over another. This set of basic data structures and algorithms will serve as an excellent foundation for building more complex and special-purpose constructs. The high-level expressiveness of Swift makes it an ideal choice for learning these core concepts without sacrificing performance.

    You’ll start with the fundamental structures of linked lists, queues and stacks, and see how to implement them in a highly Swift-like way. Move on to working with various types of trees, including general purpose trees, binary trees, AVL trees, binary search trees, and tries. Go beyond bubble and insertion sort with better-performing algorithms, including mergesort, radix sort, heap sort, and quicksort. Learn how to construct directed, non-directed and weighted graphs to represent many real-world models. Traverse those graphs and trees efficiently with breadth-first, depth-first, Dijkstra’s and Prim’s algorithms to solve problems such as finding the shortest path or lowest cost in a network.

    By the end of this book, you’ll have hands-on experience solving common issues with data structures and algorithms — and you’ll be well on your way to developing your own efficient and useful implementations!



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  • Data Persistence with SwiftData | Kodeco

    Data Persistence with SwiftData | Kodeco



    Mar 19 2025, Swift 5.10, iOS 17, ipadOS 17, macOS 15, visionOS 1.2, Xcode 15


    Leave a rating/review

    Explore advanced techniques to build robust data management solutions with SwiftData integrated with SwiftUI,
    and how to use it with CloudKit. Understand how interoperability works between SwiftData and Core Data, and learn some
    advanced data persistence techniques.

    By Tim Mitra.



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  • Data Management & Optimization | Kodeco

    Data Management & Optimization | Kodeco


    All videos. All books.
    One low price.

    Learn iOS, Swift, Android, Kotlin, Flutter and Dart development and unlock our massive
    catalogue of 50+ books and 4,000+ videos.

    Learn more

    The largest and most up-to-date collection of courses and books on iOS,
    Swift, Android, Kotlin, Flutter, Dart, Server-Side Swift, Unity, and more!

    © 2025 Kodeco Inc



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  • Break Down CX Data Silos for Company-Wide Impact

    Break Down CX Data Silos for Company-Wide Impact


    The blog post references our new e-guide, “The Tech Company’s Blueprint to Leveling-Up Customer Experience”. To access the free e-guide, click here.  

    Customer experience (CX) is too often seen as the responsibility of just one department—usually customer success, service, or experience teams. But the reality is that CX insights impact every part of a tech company, from product development to marketing to executive decision-making. 

    When customer insights remain trapped within specific departments, other teams miss out on valuable feedback that could inform their strategies. To truly harness the power of CX, software companies must break down these silos and ensure that insights flow seamlessly across all teams. 

    Why breaking down CX data siloes matters 

    When teams operate in silos, they miss out on key information that could improve their decision-making. Here’s what happens when customer feedback is not widely shared across the company: 

    • Product teams might not hear about common usability complaints until they become major churn risks. 
    • Marketing teams could miss opportunities to highlight features customers love (or to address messaging gaps). 
    • Sales teams lack insights into why prospects hesitate to buy—or why they churn later. 
    • Executives struggle to measure the real impact of CX investments on retention and revenue. 

    By making CX a company-wide responsibility, businesses can proactively enhance the customer journey, instead of reacting to problems after they escalate. 

    Start here: Sharing feedback across teams 

    Breaking down data siloes starts with ensuring CX insights flow across the organization in structured, actionable ways. Here’s how to get started: 

    1. Secure buy-In from leadership 

    If CX insights are stuck at the bottom of the organization, change won’t happen. Leadership must champion customer feedback as a key business driver—not just a support metric. 

    • Position CX as a revenue driver by correlating improvements with retention, expansion, and revenue growth. 
    • Involve executives in reviewing customer insights regularly. 
    • Tie CX metrics to company-wide KPIs to make them part of performance evaluations. 

    2. Assign a CX champion 

    To prevent feedback from getting lost, designate a CX data champion (or team) to: 

    • Oversee customer insight distribution across the company. 
    • Ensure feedback is routed to the right teams at the right time. 
    • Act as a bridge between departments to keep everyone aligned. 

    3. Identify key stakeholders in every department 

    Each team in a tech company benefits from CX data in different ways: 

    • Product teams need usability, feature request, and bug-related insights. 
    • Marketing teams need feedback on messaging clarity, brand perception, and differentiation. 
    • Sales teams need insights into friction points, dealbreakers, and purchase drivers. 
    • Support teams need real-time feedback to optimize responses and reduce ticket volumes. 

    By identifying key stakeholders and customizing feedback for their needs, CX becomes more relevant and actionable across teams. 

    4. Establish a single source of truth 

    Customer feedback should live in one centralized location—not scattered across emails, spreadsheets, or standalone reports. 

    • Ensure all departments have access to feedback insights. 
    • Standardize how feedback is categorized to make analysis easier. 

    5. Standardize feedback distribution processes 

    To make feedback accessible and actionable, set up structured ways to distribute it: 

    • Weekly or monthly CX reports summarizing key trends. 
    • Automated alerts for urgent issues (e.g., negative NPS responses, churn risk). 
    • Dashboards that allow teams to track insights in real-time. 
    • A structured process prevents valuable feedback from getting lost in a sea of data. 

    Continue reading  

    When customer insights are accessible to every team, tech companies create more intuitive products, craft compelling marketing messages, close more deals, and build lasting customer relationships. 

    However, making CX a company-wide priority requires the right strategy and tools. Whether you’re building your CX program from the ground up or looking to take it to the next level, “The Tech Company’s Blueprint to Leveling-Up Customer Experience,” provides a step-by-step framework for collecting customer insights and turning them into actionable improvements. 

    📥 Download the e-guide now to transform your customer experience strategy and drive measurable business impact. Click here to get your free copy! 



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  • 5 Key Data and AI Innovations to Keep an Eye on in 2025

    5 Key Data and AI Innovations to Keep an Eye on in 2025


    Opinions expressed by Entrepreneur contributors are their own.

    At the end of the first quarter in 2025, now is a good time to reflect upon the recent updates from Amazon Web Services (AWS) to their services that provide data and AI capabilities to end customers. At the end of 2024, AWS hosted 60,000+ practitioners at their annual conference, re:Invent, in Las Vegas.

    Hundreds of features and services were announced during the week; I’ve combined these with the announcements that have come since and curated five key data and AI innovations that you should take notice of. Let’s dive in.

    The next generation of Amazon SageMaker

    Amazon SageMaker has historically been seen as the center for everything AI in AWS. Services like Amazon Glue or Elastic MapReduce have taken care of data processing tasks, with Amazon Redshift picking up the task of SQL analytics. With an increasing number of organizations focusing efforts on data and AI, all-in-one platforms such as Databricks have understandably caught the eyes of those starting their journey.

    The next generation of Amazon SageMaker is AWS’s answer to these services. SageMaker Unified Studio brings together SQL analytics, data processing, AI model development and generative AI application development under one roof. This is all built on top of the foundations of another new service — SageMaker Lakehouse — with data and AI governance integrated through what previously existed standalone as Amazon DataZone.

    The promise of an AWS first-party solution for customers looking to get started with, increase the capability of, or gain better control of their data and AI workloads is exciting indeed.

    Amazon Bedrock Marketplace

    Sticking with the theme of AI workloads, I want to highlight Amazon Bedrock Marketplace. The world of generative AI is fast-moving, and new models are being developed all the time. Through Bedrock, customers can access the most popular models on a serverless basis — only paying for the input/output tokens that they use. To do this for every specialized industry model that customers may want to access is not scalable, however.

    Amazon Bedrock Marketplace is the answer to this. Previously, customers could use Amazon SageMaker JumpStart to deploy LLMs to your AWS account in a managed way; this excluded them from the Bedrock features that were being actively developed (Agents, Flows, Knowledge Bases etc.), though. With Bedrock Marketplace, customers can select from 100+ (and growing) specialized models, including those from HuggingFace and DeepSeek, deploy them to a managed endpoint and access them through the standard Bedrock APIs.

    This results in a more seamless experience and makes experimenting with different models significantly easier (including customers’ own fine-tuned models).

    Amazon Bedrock Data Automation

    Extracting insights from unstructured data (documents, audio, images, video) is something that LLMs have proven themselves to excel at. While the potential value borne from this is enormous, setting up performant, scalable, cost-effective and secure pipelines to extract this is something that can be complicated, and customers have historically struggled with it.

    In recent days — at time of writing — Amazon Bedrock Data Automation reached General Availability (GA). This service sets out to solve the exact problem I’ve just described. Let’s focus on the document use case.

    Intelligent Document Processing (IDP) isn’t a new use case for AI — it existed long before GenAI was all the rage. IDP can unlock huge efficiencies for organizations that deal in paper-based forms when augmenting or replacing the manual processes that are performed by humans.

    With Bedrock Data Automation, the heavy-lifting of building IDP pipelines is abstracted away from customers and provided as a managed service that’s easy to consume and subsequently integrate into legacy processes and systems.

    Amazon Aurora DSQL

    Databases are an example of a tool where the level of complexity exposed to those leveraging it is not necessarily correlated with how complex it is behind the scenes. Often, it’s an inverse relationship where the simpler and more “magic” a database is to use, the more complex it is in the areas that are unseen.

    Amazon Aurora DSQL is a great example of such a tool where it’s as straightforward to use as AWS’s other managed database services, but the level of engineering complexity to make its feature set possible is huge. Speaking of its feature set, let’s look at that.

    Aurora DSQL sets out to be the service of choice for workloads that need durable, strongly consistent, active-active databases across multiple regions or availability zones. Multi-region, or multi-AZ databases, are already well established in active-passive configurations (i.e., one writer and many read-replicas); active-active is a problem that’s much harder to solve while still being performant and retaining strong consistency.

    If you’re interested in reading the deep technical details of challenges that were overcome in the building of this service, I’d recommend reading Marc Brooker’s (Distinguished Engineer at AWS) series of blog posts on the topic.

    When announcing the service, AWS described it as providing “virtually unlimited horizontal scaling with the flexibility to independently scale reads, writes, compute, and storage. It automatically scales to meet any workload demand without database sharding or instance upgrades. Its active-active distributed architecture is designed for 99.99% single-Region and 99.999% multi-Region availability with no single point of failure, and automated failure recovery.”

    For organizations where global scale is an aspiration or requirement, building on top of a foundation of Aurora DSQL sets them up very nicely.

    Expansion of zero-ETL features

    AWS has been pushing the “zero-ETL” vision for a couple of years now, with the aspiration being to make moving data between purpose-built services as easy as possible. An example would be moving transactional data from a PostgreSQL database running on Amazon Aurora to a database designed for large-scale analytics like Amazon Redshift.

    While there has been a relatively continuous flow of new announcements in this area, the end of 2024 and start of 2025 saw a flurry that accompanied the new AWS services released at re:Invent.

    There are far too many to talk about here in any level of detail that’d provide value; to find out more about all of the available zero-ETL integrations between AWS services, please visit AWS’s dedicated zero-ETL page.

    Wrapping this up, we’ve covered five areas relating to data and AI that AWS is innovating in to make building, growing and streamlining organizations easier. All of these areas are relevant to small and growing startups, as well as billion-dollar enterprises. AWS and other cloud service providers are there to abstract away the complexity and heavy lifting, leaving you to focus on building your business logic.



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  • New ways to unlock revenue with first-party data

    New ways to unlock revenue with first-party data


    Another solution to help you unlock the value of first-party data is app analytics connection. It links your first-party analytics data like user activity and purchase events to publisher first-party ID to further enhance ad personalization. App analytics connection is fully integrated with Google Analytics for Firebase.

    Publishers who have been testing these first-party data solutions are excited about the impact they’ve seen.

    “We were particularly interested in user insight surveys, which can help us generate first-party data while players are engaging with our games to personalize ad serving in the absence of third-party tracking IDs. We’re excited about AdMob’s commitment and progress in developing first-party data solutions. It gives us confidence that they will continue to build durable solutions to help advance our monetization strategy in the long run.”

    — Kiel LeBaron, Sr. Director, Ad Monetization, Jam City

    “It’s been a core focus of my team to identify ways to unlock new revenue growth with less reliance on third-party identifiers. I’m excited about the early results of AdMob’s simple and automatic solutions like app analytics connection and user insight surveys, which are enabling me to start using a first-party data strategy on AdMob to boost my revenue potential.”

    — Diogo Branco, Head of Ads Monetization, MiniClip

    User insight surveys and app analytics connection are currently in beta and will be available to more AdMob publishers in the near future. Reach out to your account manager to learn more or follow AdMob channels to get the latest information.



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