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A summary of sessions at the first DataEngineering Open Forum at Netflix on April 18th, 2024 The DataEngineering Open Forum at Netflix on April 18th, 2024. At Netflix, we aspire to entertain the world, and our dataengineering teams play a crucial role in this mission by enabling data-driven decision-making at scale.
As a micro-service owner, a Netflix engineer is responsible for its innovation as well as its operation, which includes making sure the service is reliable, secure, efficient and performant. How can we develop templated detection modules (rules- and ML-based) and data streams to increases speed of development?
Full ownership often means building new data pipelines, navigating complex schemas and large data sets, developing or improving metrics for business performance, and creating intuitive visualizations and dashboards?—?always Others have grown into new areas as part of their professional development at Netflix.
We adopted the following mission statement to guide our investments: “Provide a complete and accurate data lineage system enabling decision-makers to win moments of truth.” Nonetheless, Netflix data landscape (see below) is complex and many teams collaborate effectively for sharing the responsibility of our data system management.
Our goal is to manage security risks to Netflix via clear, opinionated security guidance, and by providing risk context to Netflix engineering teams to make pragmatic risk decisions at scale. including bug bounty, pentesting, PSIRT (product security incident response), security reviews, and developer security education?—?via
In addition to Spark, we want to support last-mile data processing in Python, addressing use cases such as feature transformations, batch inference, and training. Occasionally, these use cases involve terabytes of data, so we have to pay attention to performance. Internally, we use a production workflow orchestrator called Maestro.
As Big data and ML became more prevalent and impactful, the scalability, reliability, and usability of the orchestrating ecosystem have increasingly become more important for our data scientists and the company. Another dimension of scalability to consider is the size of the workflow.
1pm-2pm NFX 207 Benchmarking stateful services in the cloud Vinay Chella , Data Platform Engineering Manager Abstract : AWS cloud services make it possible to achieve millions of operations per second in a scalable fashion across multiple regions. We explore all the systems necessary to make and stream content from Netflix.
At this scale, we can gain a significant amount of performance and cost benefits by optimizing the storage layout (records, objects, partitions) as the data lands into our warehouse. Some of the optimizations are prerequisites for a high-performance data warehouse. Other Components Iceberg We use Apache Iceberg as the table format.
Whether in analyzing A/B tests, optimizing studio production, training algorithms, investing in content acquisition, detecting security breaches, or optimizing payments, well structured and accurate data is foundational. Users configure the workflow to read the data in a window (e.g. data arrives too late to be useful).
You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc. Please apply here. Advertise your job here!
You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc. Please apply here. Advertise your job here!
You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc. Please apply here. Advertise your job here!
Zendesk reduced its data storage costs by over 80% by migrating from DynamoDB to a tiered storage solution using MySQL and S3. The company considered different storage technologies and decided to combine the relational database and the object store to strike a balance between querybility and scalability while keeping the costs down.
Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows dataengineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.
You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc. Created by former senior-level AWS engineers of 15 years.
Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows dataengineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.
Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows dataengineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.
Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows dataengineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.
This event is designed to help senior developers navigate their immediate development challenges, focusing exclusively on the technical aspects that matter right now. InfoQ is delighted to announce a new two-day conference, InfoQ Dev Summit Boston 2024, taking place June 24-25, 2024. By Artenisa Chatziou
Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows dataengineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.
Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows dataengineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.
Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows dataengineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.
Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows dataengineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.
Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows dataengineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.
You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc. Please apply here. Try the 30-day free trial!
Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows dataengineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.
You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc. Please apply here. Try the 30-day free trial!
1pm-2pm NFX 207 Benchmarking stateful services in the cloud Vinay Chella , Data Platform Engineering Manager Abstract : AWS cloud services make it possible to achieve millions of operations per second in a scalable fashion across multiple regions. We explore all the systems necessary to make and stream content from Netflix.
1pm-2pm NFX 207 Benchmarking stateful services in the cloud Vinay Chella , Data Platform Engineering Manager Abstract : AWS cloud services make it possible to achieve millions of operations per second in a scalable fashion across multiple regions. We explore all the systems necessary to make and stream content from Netflix.
AWS recently announced the general availability (GA) of Amazon EC2 P5 instances powered by the latest NVIDIA H100 Tensor Core GPUs suitable for users that require high performance and scalability in AI/ML and HPC workloads. The GA is a follow-up to the earlier announcement of the development of the infrastructure.
Adding architects and engineers, we see that roughly 55% of the respondents are directly involved in software development. Technical roles represented in the “Other” category include IT managers, dataengineers, DevOps practitioners, data scientists, systems engineers, and systems administrators.
Data Pipeline A data pipeline is a software that ingests data from multiple sources, transforms it and finally makes it available to internal or external products. Unfortunately, building data pipelines remains a daunting, time-consuming, and costly activity. Depending on frameworks, data processing units (a.k.a
In such a data intensive environment, making key business decisions such as running marketing and sales campaigns, logistic planning, financial analysis and ad targeting require deriving insights from these data. However, the data infrastructure to collect, store and process data is geared toward developers (e.g.,
Uber uses Presto, an open-source distributed SQL query engine, to provide analytics across several data sources, including Apache Hive, Apache Pinot, MySQL, and Apache Kafka. To improve its performance, Uber engineers explored the advantages of dealing with quick queries, a.k.a.
Kubernetes has emerged as go to container orchestration platform for dataengineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Key challenges. Performance.
When it comes to deploying Large Language Models (LLMs) in production, the two major challenges originate from the huge amount of parameters they require and the necessity of handling very long input sequences to represent contextual information.
HubSpot adopted routing messages over multiple Kafka topics (called swimlanes) for the same producer to avoid the build-up in the consumer group lag and prioritize the processing of real-time traffic.
LinkedIn introduced Couchbase as a centralized caching tier for scaling member profile reads to handle increasing traffic that has outgrown their existing database cluster. The new solution achieved over 99% hit rate, helped reduce tail latencies by more than 60% and costs by 10% annually. By Rafal Gancarz
Microsoft recently announced the general availability (GA) of Azure Managed Lustre, a managed file system for high-performance computing (HPC) and AI workloads. By Steef-Jan Wiggers
This diverse technological landscape generates extensive and rich data from various infrastructure entities, from which, dataengineers and analysts collaborate to provide actionable insights to the engineering organization in a continuous feedback loop that ultimately enhances the business.
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