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Yahoo
6 days ago
- Business
- Yahoo
The Fourth Generation Rises: How Singdata's Lakehouse is Defining the General Incremental Compute Standard and Revolutionizing Data Processing Architecture
BEIJING, July 24, 2025 /PRNewswire/ -- The data processing landscape is experiencing a seismic shift. As enterprises grapple with exponentially growing data volumes and increasingly complex real-time requirements, traditional architectures are reaching their breaking point. Today, a new paradigm is emerging—one that promises to fundamentally reshape how we think about data processing efficiency, cost optimization, and architectural design. At the forefront of this transformation stands Singdata, the pioneering force behind General Incremental Compute (GIC), a revolutionary approach that represents the fourth generation of data processing architecture. Far from being merely another Flink alternative, GIC introduces a paradigm shift that addresses the fundamental limitations plaguing modern data infrastructure. The Flink Era: Hitting the Ceiling of Traditional Stream Processing Apache Flink has undoubtedly been a cornerstone of real-time data processing, excelling in millisecond-latency scenarios like real-time dashboards, fraud detection, and programmatic advertising. However, as AI-driven applications proliferate and data volumes reach unprecedented scales, Flink's architectural constraints have become increasingly apparent. The most significant limitation lies in computational scope. While Flink dominates millisecond-response scenarios, the vast majority of enterprise use cases—user behavior analytics, business metric monitoring, and ML model observability—operate in minute-to-hour timeframes. For these scenarios, Flink's continuous resource occupation model proves economically inefficient, creating an unsustainable cost structure that forces organizations into difficult trade-offs between real-time insights and operational expenses. Data processing fragmentation compounds these challenges. Enterprises must maintain separate Flink and Spark infrastructures for stream and batch processing, respectively. This dual-engine approach introduces syntax incompatibilities, forces development teams to master multiple technology stacks, and creates consistency nightmares where identical metrics produce different results across systems. The operational overhead of maintaining these parallel architectures has become a significant barrier to digital transformation initiatives. Development complexity further exacerbates the problem. Implementing sophisticated operations like dimension table joins, state management, and windowed computations in Flink requires extensive boilerplate code and deep understanding of internal mechanisms. Every business requirement change demands significant engineering effort, making agile data product development nearly impossible for most organizations. Perhaps most critically, Flink's resource utilization model reveals fundamental architectural flaws. Stream processing requires persistent resource allocation regardless of data flow volume, creating massive inefficiencies during low-traffic periods. As data volumes scale, resource consumption grows exponentially, forcing enterprises into an untenable choice between real-time capabilities and cost control. Singdata's Vision: Establishing Technical Authority in Incremental Computing Recognizing these systemic limitations, Singdata has emerged as the definitive standard-setter in incremental computing, establishing both conceptual framework and technical implementation standards for the industry. Since 2023, Singdata has pioneered the "incremental computing" concept, evolving it into the comprehensive "General Incremental Compute" framework by 2025. This progression represents more than incremental innovation—it constitutes a fundamental rethinking of data processing architecture that addresses the core inefficiencies of existing approaches. Central to Singdata's leadership is the SPOT standard—a comprehensive framework encompassing Standard SQL for unified syntax, Performance optimization for efficiency gains, Open Format compatibility with ecosystems like Apache Iceberg, and Trade-offs for flexible cost-performance balancing. This standard doesn't merely solve current pain points; it establishes a roadmap for industry-wide architectural evolution. Singdata's strategic approach to ecosystem development demonstrates remarkable technical confidence. Rather than pursuing proprietary lock-in strategies, the company has embraced collaborative ecosystem development, accelerating technology adoption while reinforcing its position as the authoritative voice in incremental computing standards. The Fourth Generation Advantage: Architectural Breakthrough Singdata represents a generational leap in data processing architecture, delivering unprecedented capabilities that traditional Lambda architectures cannot match. The core innovation lies in computational methodology. While Flink employs "continuous processing" requiring persistent resource allocation and batch systems use "full recalculation" with poor latency characteristics, incremental computing implements "compute-on-need" processing. This approach processes only data deltas, achieving minute-level freshness while eliminating persistent resource occupation—delivering cost efficiency improvements of several orders of magnitude. Unified technology stack integration eliminates the architectural fragmentation that plagues traditional systems. Beyond supporting standard SQL syntax, incremental computing enables truly unified stream-batch development. Complex operations like real-time dimension joins and sophisticated queries execute through simple SQL statements, improving development efficiency by an order of magnitude. Open format ecosystem compatibility breaks down vendor lock-in barriers. Built on Apache Iceberg and other open data lake formats, Singdata lakehouse architecture integrates seamlessly with existing AI data ecosystems. Organizations can leverage new capabilities without massive technology migrations, dramatically reducing adoption barriers. Flexible scheduling capabilities provide unprecedented resource configuration freedom. Enterprises can adjust processing frequency from one minute to several hours based on business requirements, optimizing the balance between data freshness and computational costs—a flexibility impossible with traditional stream processing. Redefining the Boundaries of Data Processing Singdata's innovation transcends Flink replacement, fundamentally redefining data processing paradigms and expanding the realm of what's possible in real-time analytics. The diversification of technical choices proves that real-time data processing no longer follows a single path. Organizations can now select optimal solutions based on specific latency requirements: millisecond scenarios continue leveraging stream processing, while minute and hour-scale scenarios benefit from incremental computing, with seamless transitions to daily batch processing when appropriate. This technological pathway diversity provides enterprises with unprecedented choice and flexibility. In cost-efficiency optimization, incremental computing achieves what traditional technologies considered impossible—simultaneously delivering real-time capabilities, cost control, and high performance. This breakthrough unlocks real-time data processing benefits for organizations previously constrained by resource limitations, democratizing advanced analytics capabilities across enterprise segments. The open ecosystem approach positions Singdata's SPOT standard as the emerging industry benchmark. Major technology vendors are increasingly focusing on incremental computing, with companies like Alibaba and Tencent making significant investments in this space. This ecosystem convergence effect accelerates industry-wide migration toward fourth-generation data processing paradigms. Leading the Revolution The successful adoption by Rednote demonstrates real-world validation of GIC's capabilities, proving that this isn't merely theoretical advancement but practical transformation delivering measurable business value. As more organizations recognize the limitations of traditional architectures and seek sustainable paths to real-time analytics, incremental computing emerges as the definitive solution. Singdata stands at the epicenter of this transformation, not merely as a technology provider but as the architect of an entirely new category. By establishing technical standards, fostering ecosystem development, and proving real-world viability, Singdata is reshaping the fundamental assumptions underlying modern data infrastructure. The fourth generation of data processing has arrived, and it promises to be as transformative as the shift from batch to stream processing a decade ago. Organizations that recognize this paradigm shift and embrace incremental computing will find themselves with sustainable competitive advantages in the data-driven economy. Those that don't risk being left behind by an industry moving inexorably toward more efficient, cost-effective, and flexible data processing architectures. The revolution has begun, and Singdata is leading the charge. View original content to download multimedia: SOURCE Singdata Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Time Business News
08-07-2025
- Business
- Time Business News
Data Engineering Services for Scalable Digital Products
In an era defined by data-driven decision-making and real-time insights, businesses face the growing challenge of managing complex and large-scale data ecosystems. As the backbone of digital transformation, data engineering services, and big data engineering services enable organizations to design, build, and operate robust data infrastructure that powers next-generation digital products. Data engineering services refer to a suite of specialized practices that focus on the architecture, development, deployment, and optimization of data pipelines and ecosystems. These services handle everything from raw data ingestion and transformation to data storage, orchestration, and governance. Their goal is to ensure that data is clean, accessible, timely, and useful for a wide range of business applications. Data Pipeline Development (ETL/ELT): Automating the extraction, transformation, and loading of data across various systems. Automating the extraction, transformation, and loading of data across various systems. Data Architecture: Designing scalable systems that unify structured and unstructured data. Designing scalable systems that unify structured and unstructured data. Data Quality & Validation: Ensuring data accuracy, completeness, and consistency. Ensuring data accuracy, completeness, and consistency. Data Integration: Merging disparate data sources to enable 360-degree visibility. Merging disparate data sources to enable 360-degree visibility. Orchestration & Workflow Management: Coordinating tasks and processes with tools like Apache Airflow or Dagster. Coordinating tasks and processes with tools like Apache Airflow or Dagster. DataOps & DevOps for Data: Enhancing collaboration, automation, and reliability across teams. Big data engineering services specifically cater to high-volume, high-velocity, and high-variety data ecosystems. These services involve: Distributed Computing: Leveraging platforms like Hadoop, Spark, and Flink for massive-scale data processing. Leveraging platforms like Hadoop, Spark, and Flink for massive-scale data processing. Stream Processing: Real-time data pipeline development using Kafka, Pulsar, or Flink. Real-time data pipeline development using Kafka, Pulsar, or Flink. Data Lakehouse Implementation: Combining the best of data lakes and warehouses for seamless analytics. Combining the best of data lakes and warehouses for seamless analytics. Machine Learning Infrastructure: Enabling model training and deployment with robust, data-ready pipelines. With big data engineering, enterprises can process petabytes of information efficiently and enable AI/ML initiatives, predictive analytics, and operational intelligence. For digital products to scale, they must be supported by infrastructure that can grow, adapt, and deliver data in real-time. Here's how data engineering services power product scalability: Scalability: Cloud-native architectures using AWS, Azure, or GCP provide elastic compute and storage. Cloud-native architectures using AWS, Azure, or GCP provide elastic compute and storage. Speed: Optimized ETL/ELT pipelines reduce latency in data delivery. Optimized ETL/ELT pipelines reduce latency in data delivery. Reliability: Redundancy and monitoring reduce downtime and data loss. Redundancy and monitoring reduce downtime and data loss. Security: Compliance with data regulations (GDPR, HIPAA) through encrypted pipelines and access controls. Compliance with data regulations (GDPR, HIPAA) through encrypted pipelines and access controls. Flexibility: Modular architecture allows integration with BI tools, data science platforms, and third-party systems. Data Ingestion Frameworks: Kafka, Apache NiFi, Fluentd Transformation Engines: Apache Beam, dbt, Spark Data Storage Solutions: Amazon S3, Google BigQuery, Snowflake, Delta Lake Workflow Orchestration: Apache Airflow, Prefect, Dagster Monitoring & Observability: Prometheus, Grafana, OpenTelemetry Versioning & CI/CD for Data: DVC, Git, Jenkins Real-time fraud detection Credit scoring and loan risk analysis Transaction analytics for customer insights Integration of EHR, wearable, and clinical data Predictive diagnostics using historical data Patient 360-degree view through unified platforms Inventory forecasting and dynamic pricing Omnichannel behavior tracking Personalized marketing and product recommendation engines Start with Business Goals: Align your data infrastructure with KPIs and product outcomes. Align your data infrastructure with KPIs and product outcomes. Adopt Modular Architecture: Use plug-and-play components to evolve with tech advancements. Use plug-and-play components to evolve with tech advancements. Automate Everything: Orchestration, testing, and monitoring ensure scalability and reliability. Orchestration, testing, and monitoring ensure scalability and reliability. Enable Data Governance: Implement lineage, cataloging, and privacy compliance early. Implement lineage, cataloging, and privacy compliance early. Invest in Skilled Teams or Partners: Choose providers who understand both domain and technology. A reliable big data engineering services partner should offer: Domain expertise: Understanding of your industry-specific data requirements Understanding of your industry-specific data requirements Toolchain fluency: Expertise in open-source and enterprise-grade tools Expertise in open-source and enterprise-grade tools Scalability know-how: Proven ability to handle complex, high-throughput systems Proven ability to handle complex, high-throughput systems Cloud-native capabilities: Experience across AWS, Azure, and GCP ecosystems Experience across AWS, Azure, and GCP ecosystems AI/ML enablement: Readiness to support machine learning infrastructure At Azilen Technologies, we offer full-spectrum data engineering services tailored to enterprise needs. Our teams specialize in building cloud-native, resilient, and high-performance data platforms that empower businesses to unlock actionable insights. Pre-built accelerators for faster time-to-market Domain-specific solutions for FinTech, HealthTech, and Retail Deep expertise in Apache Spark, Kafka, Snowflake, Databricks End-to-end DevOps, DataOps, and MLOps integration Scalable architecture advisory and implementation In 2025 and beyond, data engineering is no longer just a support function—it's a strategic enabler of digital growth. Organizations that invest in robust data engineering services and big data engineering services are better equipped to innovate, scale, and remain competitive in a rapidly evolving digital economy. 1. What are data engineering services? Data engineering services involve designing and building systems that collect, process, and store large volumes of data for analytics and business decision-making. 2. How do big data engineering services differ from traditional data engineering? Big data engineering focuses on processing massive datasets using distributed computing technologies, while traditional data engineering handles smaller, often relational datasets. 3. Why are data engineering services essential for enterprise digital products? They enable real-time insights, personalized experiences, and operational efficiency by ensuring timely access to accurate data across applications. 4. What technologies are commonly used in data engineering services? Popular tools include Apache Spark, Kafka, Airflow, dbt, Snowflake, BigQuery, and AWS/GCP/Azure cloud-native platforms. 5. How can data engineering services support machine learning initiatives? By building data pipelines that feed clean, reliable, and labeled data into ML training and inference workflows. 6. What industries benefit most from big data engineering services? FinTech, HealthTech, Retail, Manufacturing, and Logistics are some of the sectors that rely heavily on large-scale data engineering. 7. How do I choose the right data engineering services partner? Look for providers with domain expertise, scalable architecture experience, open-source fluency, and a track record of enterprise-grade implementations. TIME BUSINESS NEWS
DW
03-07-2025
- Science
- DW
DNA shows ancient Egyptians had surprising foreign roots – DW – 07/02/2025
The ancient Egyptian potter lived about 4,500 years ago. It's hoped his DNA will open the way to a better understanding of the country's genetic history. Ancient Egypt went through a period of major change between 4,500-4,800 years ago. The country's Early Dynastic period was transitioning into the Old Kingdom period. This saw advances that allowed expert builders in Cairo to construct what would become the Great Pyramid of Giza. It also saw mature hieroglyphic writing and the emergence of the pottery wheel. South of Cairo, in a village called Nuwayrat, one man lived a hard life as a potter, even with the new technology. But, fortunately, when he died, his body was placed in a ceramic pot and buried in a tomb cut into a hillside, allowing UK-based researchers to analyze his remains, genetically. Their study, published in the journal Nature, describes the first whole ancient Egyptian genome, and the oldest DNA sample from Egypt to date. "This individual lived and died during a critical period of change in Ancient Egypt," said Linus Girdland Flink, a biomolecular archaeologist at the University of Aberdeen, in the UK, and co-senior author on the study. Flink and colleagues have revealed how the potter lived and died, and his genetic ancestry. They know he stood 1.6 meters (5.2 feet) tall, had brown eyes and brown hair, and lived to be as old as 64 years. "We've been able to tell part of the individual's story, finding that some of his ancestry came from the Fertile Crescent, highlighting a mixing of groups [from North Africa and the Middle East] at this time," said Girdland Flink. The Fertile Crescent was where the first agricultural communities of the Middle East and Mediterranean basin are thought to have settled. It was a crescent-shaped region that spanned modern-day Syria, southeastern Turkey and Iraq. While it is difficult to draw broad conclusions from a single individual, "this groundbreaking article provides a first glimpse into the genetics of early Egypt, a region that has long been a critical gap in the ancient DNA map," said Iosif Lazaridis, a geneticist at Harvard University in the US, who was not involved in the study. The researchers first analyzed the man's skeleton with a variety of techniques to find clues about his life. Using radiocarbon dating, they confirmed he lived at some point between 2,855-2,570 B.C., a time overlapping the Early Dynastic and Old Kingdom periods. They ran a chemical analysis of the man's teeth to learn about his diet. The results suggested the individual had likely grown up in Egypt. And markings on the skeleton itself gave clues that he could have worked as a potter. His seat bones were expanded in size, his arms showed evidence of extensive movement back and forth and there's substantial arthritis, only in his right foot. "Though circumstantial, these clues point towards pottery, including use of a pottery wheel, which arrived in Egypt around the same time," said Joel Irish, an archaeologist at Liverpool John Moores University, UK, and co-author of the study. But his higher-class burial was unusual for a potter of that time. "Perhaps he was exceptionally skilled or successful to advance his social status," Irish said. Scientists have sequenced the DNA of Egyptian mummies before, but these individuals lived during the late intermediate period after 1,400 B.C. The potter is thought to be at least 1,000 years older. "We had no ancient Egyptian DNA. This [study] is a completely new genetic analysis of someone from the old Kingdom," said Harald Ringbauer, a population geneticist at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, who was not involved in the study. "A major problem with previous attempts was that samples were mummified, which contaminates DNA. Here, with a normal burial, the DNA was well-preserved. This makes it special," Ringbauer told DW. Extracting DNA from the man's tooth, the researchers sequenced the man's whole genome. Analysis showed that 80% of his ancestry was related to ancient individuals who lived in North Africa. The remaining 20% of his ancestry was traced to people who lived in the Fertile Crescent, particularly Mesopotamia. "It's a big open question: people with Levantine ancestry, who brought farming from the Fertile Crescent, came to Egypt. The authors speculate the Levantine ancestry came relatively late, so this study is the first major step to answering this question," said Ringbauer. This browser does not support the video element. Genetic evidence suggesting that people moved into Egypt and mixed with local populations at this time was previously only visible in archaeological findings. But researchers lack diversity in genome sequencing, and Ringbauer said that was still a problem. "We don't have any ancient DNA to compare this sample to, so we don't know how much of their ancestry is local," said Ringbauer. The authors say their study shows it's possible to provide strong genetic evidence of the movements of people in Egypt during the Bronze Age. Lazaridis agreed the study marked an advance in recovering DNA from ancient Egyptians. "For the first time, the genetic history of Ancient Egypt can truly begin to be written," he said. In future work, the research team hopes to build a bigger picture of migration and ancestry in collaboration with Egyptian source:
DW
03-07-2025
- Science
- DW
DNA shows ancient Egyptians had surprising foreign roots – DW – 07/02/2025
The ancient Egyptian potter lived about 4,500 years ago. It's hoped his DNA will open the way to a better understanding of the country's genetic history. Ancient Egypt went through a period of major change between 4,500-4,800 years ago. The country's Early Dynastic period was transitioning into the Old Kingdom period. This saw advances that allowed expert builders in Cairo to construct what would become the Great Pyramid of Giza. It also saw mature hieroglyphic writing and the emergence of the pottery wheel. South of Cairo, in a village called Nuwayrat, one man lived a hard life as a potter, even with the new technology. But, fortunately, when he died, his body was placed in a ceramic pot and buried in a tomb cut into a hillside, allowing UK-based researchers to analyze his remains, genetically. Their study, published in the journal , describes the first whole ancient Egyptian genome, and the oldest DNA sample from Egypt to date. "This individual lived and died during a critical period of change in Ancient Egypt," said Linus Girdland Flink, a biomolecular archaeologist at the University of Aberdeen, in the UK, and co-senior author on the study. Flink and colleagues have revealed how the potter lived and died, and his genetic ancestry. They know he stood 1.6 meters (5.2 feet) tall, had brown eyes and brown hair, and lived to be as old as 64 years. "We've been able to tell part of the individual's story, finding that some of his ancestry came from the Fertile Crescent, highlighting a mixing of groups [from North Africa and the Middle East] at this time," said Girdland Flink. The Fertile Crescent was where the first agricultural communities of the Middle East and Mediterranean basin are thought to have settled. It was a crescent-shaped region that spanned modern-day Syria, southeastern Turkey and Iraq. While it is difficult to draw broad conclusions from a single individual, "this groundbreaking article provides a first glimpse into the genetics of early Egypt, a region that has long been a critical gap in the ancient DNA map," said Iosif Lazaridis, a geneticist at Harvard University in the US, who was not involved in the study. The researchers first analyzed the man's skeleton with a variety of techniques to find clues about his life. Using radiocarbon dating, they confirmed he lived at some point between 2,855-2,570 B.C., a time overlapping the Early Dynastic and Old Kingdom periods. They ran a chemical analysis of the man's teeth to learn about his diet. The results suggested the individual had likely grown up in Egypt. And markings on the skeleton itself gave clues that he could have worked as a potter. His seat bones were expanded in size, his arms showed evidence of extensive movement back and forth and there's substantial arthritis, only in his right foot. "Though circumstantial, these clues point towards pottery, including use of a pottery wheel, which arrived in Egypt around the same time," said Joel Irish, an archaeologist at Liverpool John Moores University, UK, and co-author of the study. But his higher-class burial was unusual for a potter of that time. "Perhaps he was exceptionally skilled or successful to advance his social status," Irish said. Scientists have sequenced the DNA of Egyptian mummies before, but these individuals lived during the late intermediate period after 1,400 B.C. The potter is thought to be at least 1,000 years older. "We had no ancient Egyptian DNA. This [study] is a completely new genetic analysis of someone from the old Kingdom," said Harald Ringbauer, a population geneticist at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, who was not involved in the study. "A major problem with previous attempts was that samples were mummified, which contaminates DNA. Here, with a normal burial, the DNA was well-preserved. This makes it special," Ringbauer told DW. Extracting DNA from the man's tooth, the researchers sequenced the man's whole genome. Analysis showed that 80% of his ancestry was related to ancient individuals who lived in North Africa. The remaining 20% of his ancestry was traced to people who lived in the Fertile Crescent, particularly Mesopotamia. "It's a big open question: people with Levantine ancestry, who brought farming from the Fertile Crescent, came to Egypt. The authors speculate the Levantine ancestry came relatively late, so this study is the first major step to answering this question," said Ringbauer. To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video Genetic evidence suggesting that people moved into Egypt and mixed with local populations at this time was previously only visible in archaeological findings. But researchers lack diversity in genome sequencing, and Ringbauer said that was still a problem. "We don't have any ancient DNA to compare this sample to, so we don't know how much of their ancestry is local," said Ringbauer. The authors say their study shows it's possible to provide strong genetic evidence of the movements of people in Egypt during the Bronze Age. Lazaridis agreed the study marked an advance in recovering DNA from ancient Egyptians. "For the first time, the genetic history of Ancient Egypt can truly begin to be written," he said. In future work, the research team hopes to build a bigger picture of migration and ancestry in collaboration with Egyptian source:
DW
02-07-2025
- Science
- DW
Life of ancient Egyptian potter revealed in DNA analysis – DW – 07/02/2025
The ancient Egyptian potter lived about 4,500 years ago. It's hoped his DNA will open the way to a better understanding of the country's genetic history. Ancient Egypt went through a period of major change between 4,500-4,800 years ago. The country's Early Dynastic period was transitioning into the Old Kingdom period. This saw advances that allowed expert builders in Cairo to construct what would become the Great Pyramid of Giza. It also saw mature hieroglyphic writing and the emergence of the pottery wheel. South of Cairo, in a village called Nuwayrat, one man lived a hard life as a potter, even with the new technology. But, fortunately, when he died, his body was placed in a ceramic pot and buried in a tomb cut into a hillside, allowing UK-based researchers to analyze his remains, genetically. Their study, published in the journal , describes the first whole ancient Egyptian genome, and the oldest DNA sample from Egypt to date. "This individual lived and died during a critical period of change in Ancient Egypt," said Linus Girdland Flink, a biomolecular archeologist at the University of Aberdeen, UK, and co-senior author on the study. Flink and colleagues have revealed how the potter lived and died, and his genetic ancestry. They know he stood 1.6 meters (5.2 feet) tall, had brown eyes, brown hair, and lived to be as old as 64 years. "We've been able to tell part of the individual's story, finding that some of his ancestry came from the Fertile Crescent, highlighting a mixing of groups [from North Africa and the Middle East] at this time," said Fink. The Fertile Crescent was where the first agricultural communities of the Middle East and Mediterranean basin are thought to have settled. It was a crescent-shaped region that spanned modern day Syria, southeastern Turkey, and Iraq. While it is difficult to draw broad conclusions from a single individual, "this groundbreaking article provides a first glimpse into the genetics of early Egypt, a region that has long been a critical gap in the ancient DNA map," said Iosif Lazaridis, a geneticist at Harvard University, US, who was not involved in the study. The researchers first analyzed the man's skeleton with a variety of techniques to find clues about his life. Using radiocarbon dating, they confirmed he lived at some point between 2,855-2,570 BCE, a time overlapping the Early Dynastic and Old Kingdom periods. They ran a chemical analysis of the man's teeth to learn about his diet. The results suggested the individual had likely grown up in Egypt. And markings on the skeleton itself gave clues that he could have worked as a potter. His seat bones were expanded in size, his arms showed evidence of extensive movement back and forth, and there's substantial arthritis, only in his right foot. "Though circumstantial, these clues point towards pottery, including use of a pottery wheel, which arrived in Egypt around the same time," said Joel Irish, an archaeologist at Liverpool John Moores University, UK, and co-author of the study. But his higher-class burial was unusual for a potter of that time. "Perhaps he was exceptionally skilled or successful to advance his social status," Irish said. Scientists have sequenced the DNA of Egyptian mummies before, but these individuals lived during the late intermediate period after 1,400 BCE. The potter is thought to be at least 1,000 years older. "We had no ancient Egyptian DNA. This [study] is a completely new genetic analysis of someone from the old Kingdom," said Harald Ringbauer, a population geneticist at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, who was not involved in the study. "A major problem with previous attempts was that samples were mummified, which contaminates DNA. Here, with a normal burial, the DNA was well-preserved. This makes it special," Ringbauer told DW. Extracting DNA from the man's tooth, the researchers sequenced the man's whole genome. Analysis showed that 80% of his ancestry was related to ancient individuals who lived in North Africa. The remaining 20% of his ancestry was traced to people who lived in the Fertile Crescent, particularly Mesopotamia. "It's a big open question: people with Levantine ancestry, who brought farming from the Fertile Crescent, came to Egypt. The authors speculate the Levantine ancestry came relatively late, so this study is the first major step to answering this question," said Ringbauer. To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video Genetic evidence suggesting that people moved into Egypt and mixed with local populations at this time was previously only visible in archaeological findings. But researchers lack diversity in genome sequencing, and Ringbauer said that was still a problem. "We don't have any ancient DNA to compare this sample to, so we don't know how much of their ancestry is local," said Ringbauer. The authors say their study shows it's possible to provide strong genetic evidence of the movements of people in Egypt during the Bronze Age. Lazaridis agreed the study marked an advance in recovering DNA from ancient Egyptians. "For the first time, the genetic history of Ancient Egypt can truly begin to be written," he said. In future work, the research team hopes to build a bigger picture of migration and ancestry in collaboration with Egyptian source:
