Realtime search
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Google's realtime search system stores tens of petabytes of data across thousands of machines and processes billions of updates per day. It uses a distributed storage system called Bigtable to store the large volume of indexing data but Bigtable cannot natively support incremental updates or enforce data integrity. Google developed Percolator, a new system built on top of Bigtable, to allow incremental processing of updates through transactions and notifications at large scale while maintaining data consistency. Percolator provides the ability to perform ACID transactions over Bigtable and organize incremental computations through observer notifications.
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Тарас Кльоба "ETL — вже не актуальна; тривалі живі потоки із системою Apache...
Тарас Кльоба "ETL — вже не актуальна; тривалі живі потоки із системою Apache...
Building a Machine Learning Recommendation Engine in SQL
Building a Machine Learning Recommendation Engine in SQL
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ScaleFast Grid And Flow
A python based grid computing project. With process work-flow built in, deploy and manage simple through to complex business processes across a distributed network of dedicated or on demand commodity computers. Run command line apps, native Python, Java and .Net code.
Presto
Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.
Artur Borycki - Beyond Lambda - how to get from logical to physical - code.ta...
Teradata believes in principles of self-service, automation, and on-demand resource allocation to enable faster, more efficient, and more effective data application development and operation. The document discusses the Lambda architecture, alternatives like the Kappa architecture, and a vision for an "Omega" architecture. It provides examples of how to build real-time data applications using microservices, event streaming, and loosely coupled services across Teradata and other data platforms like Hadoop.
Big data architecture
The document discusses the key components of a big data architecture. It describes how a big data architecture is needed to handle large volumes of data from multiple sources that is too large for traditional databases. The architecture ingests data from various sources, stores it, enables both batch and real-time analysis, and delivers business insights to users. It also provides examples of Flipkart's data platform which includes components like an ingestion system, batch/streaming processing, and a messaging queue.
Batch Processing vs Stream Processing Difference
Batch processing involves processing large batches of data together, and has higher latency measured in minutes or hours. Stream processing processes continuous data in real-time with lower latency measured in milliseconds or seconds. The key differences are that batch processing handles large batches of data while stream processing handles individual records or micro-batches, and batch processing has higher latency while stream processing has lower latency.
Building High Performance MySQL Query Systems and Analytic Applications
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Building High Performance MySql Query Systems And Analytic Applications
This presentation gives practical advice and tips on how to build high-performance read intensive databases, and discusses innovations such as column-oriented databases
informatica data replication (IDR)
This document discusses data replication and Informatica's data replication solution. It defines data replication as automating the cloning of thousands of application tables in real-time while managing transaction data capture, routing, and delivery. Informatica's data replication provides continuous availability during upgrades, reduces IT costs by offloading to lower cost systems, and enables uninterrupted migrations. It replicates transactional changes between source and target systems with high extraction and apply speeds. The solution benefits data warehouses, real-time reporting, migrations, and auditing requirements.
Otimizações de Projetos de Big Data, Dw e AI no Microsoft Azure
Apresentação para a Agência Nacional de Aviação Civil sobre Otimzações de Projetos de Big Data, Dw e AI
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...
This document discusses engineering machine learning data pipelines and addresses five big challenges: 1) scattered and difficult to access data, 2) data cleansing at scale, 3) entity resolution, 4) tracking data lineage, and 5) ongoing real-time changed data capture and streaming. It presents DMX Change Data Capture as a solution to capture changes from various data sources and replicate them in real-time to targets like Kafka, HDFS, databases and data lakes to feed machine learning models. Case studies demonstrate how DMX-h has helped customers like a global hotel chain and insurance and healthcare companies build scalable data pipelines.
Sayed database system_architecture
The document discusses different types of database system architectures, including centralized systems, client-server systems, parallel systems, and distributed systems. It provides details on centralized systems, which run on a single computer, and client-server systems, where client systems generate requests that are satisfied by server systems. Transaction servers and data servers are described as two types of client-server systems. The document also covers parallel database systems that consist of multiple processors and disks connected by an interconnection network, and discusses concepts of speedup and scaleup in parallel systems.
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Online transaction processing (OLTP) systems facilitate transaction-oriented data entry and retrieval applications in real-time. OLTP provides simplicity and efficiency for businesses by reducing paper trails and enabling faster financial forecasting. Effective OLTP requires support for distributed transactions across networks and platforms using client/server architectures and transaction management software. It involves gathering input, processing it, and updating existing information. Concurrency control protocols like locking and timestamps are used to manage concurrent transactions and avoid problems such as lost updates, dirty reads, and phantoms.
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Online transaction processing (OLTP) systems facilitate transaction-oriented applications like data entry and retrieval. An example is an automated teller machine (ATM). OLTP systems aim to provide immediate responses to user requests through simplicity, efficiency, and reduced paperwork. New OLTP software uses client/server processing and transaction brokering to support transactions spanning networks and companies. Maintaining high performance for large numbers of concurrent updates requires sophisticated transaction management and database optimization.
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This document discusses different database system architectures including centralized systems, client-server systems, parallel systems, and distributed systems. It provides details on the components and functionality of centralized, client-server, and transaction server architectures. It also describes data servers, addressing issues like page shipping versus item shipping, locking, data caching, and lock caching in data server systems. The document concludes with explanations of speedup and scaleup in parallel database systems.
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Artur Borycki - Beyond Lambda - how to get from logical to physical - code.ta...
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Building High Performance MySQL Query Systems and Analytic Applications
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Otimizações de Projetos de Big Data, Dw e AI no Microsoft Azure
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Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...
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Realtime search
- 3. Realtime Search @ Twitter
- 31. Lock-free algorithms and data structures
- 47. Realtime Search @ Google
- 54. Percolator
- 55. Transaction is a must
- 56. Transaction is a must
- 57. Transaction is a must
- 66. Evaluation
- 67. Evalution