Abhishek Tiwari

DECEMBER 27, 2017

Kubernetes has emerged as go to container orchestration platform for data engineering 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.

Big Data 63

Big Data Storage Benchmarking Hardware 63

Dynatrace

JULY 5, 2022

AIOps combines big data and machine learning to automate key IT operations processes, including anomaly detection and identification, event correlation, and root-cause analysis. Improved time management and event prioritization. What is AIOps, and how does it work? Seven benefits of AIOps for operational transformation.

Artificial Intelligence 325

Artificial Intelligence Cloud Innovation Strategy 325

The Netflix TechBlog

JULY 26, 2021

With this batch style approach, several issues have surfaced like data movement is tightly coupled with database tables, database schema is not an exact mapping of business data model, and data being stale given it is not real time etc. As of now, CDC sources have been implemented for data stores at Netflix (MySQL, Postgres).

Big Data 257

Big Data Government Processing Analytics 257

The Netflix TechBlog

APRIL 29, 2019

Orchestration The Big Data Orchestration team is responsible for providing all of the services and tooling to schedule and execute ETL and Adhoc pipelines. Internally, we also built an event-driven platform that is fully written in Python. This allows us to define conditions to filter events, and actions to react or route them.

Open Source 280

Open Source Network Infrastructure Big Data 280

The Netflix TechBlog

OCTOBER 28, 2021

Netflix’s unique work culture and petabyte-scale data problems are what drew me to Netflix. During earlier years of my career, I primarily worked as a backend software engineer, designing and building the backend systems that enable big data analytics. What is your favorite project?

Data Engineering 181

Data Engineering Engineering Big Data Software Engineering 181

Dynatrace

MARCH 11, 2021

In the fourth part of the series, I’ll show you how I used Dynatrace’s raw problem and event data to find the best fit for optimized anomaly detection settings. I took a big-data-analysis approach, which started with another problem visualization. Statistically analyzing Dynatrace’s event and problem data.

Tuning 264

Tuning Architecture Monitoring Big Data 264

Dynatrace

OCTOBER 17, 2022

Artificial intelligence for IT operations, or AIOps, combines big data and machine learning to provide actionable insight for IT teams to shape and automate their operational strategy. CloudOps includes processes such as incident management and event management. CloudOps: Applying AIOps to multicloud operations.

DevOps 246

DevOps Artificial Intelligence Healthcare Innovation 246

The Netflix TechBlog

MARCH 25, 2019

In the push model paradigm, various platform tools such as the data transportation layer, reporting tools, and Presto will publish lineage events to a set of lineage related Kafka topics, therefore, making data ingestion relatively easy to scale improving scalability for the data lineage system.

Infrastructure 225

Infrastructure Big Data Transportation Architecture 225

Dynatrace

JANUARY 16, 2023

Messaging : RabbitMQ and Kafka are the two main messaging and event streaming systems used. Big data : To store, search, and analyze large datasets, 32% of organizations use Elasticsearch. Accordingly, for classic database use cases, organizations use a variety of relational databases and document stores.

Open Source 358

Open Source Java Operating System Programming 358

Dynatrace

APRIL 20, 2023

Further, business leaders must often determine whether the data is relevant for the business and if they can afford it. Logs are automatically produced and time-stamped documentation of events relevant to cloud architectures. Dynatrace Grail unifies data from logs, metrics, traces, and events within a real-time model.

Analytics 246

Analytics Infrastructure Storage Architecture 246

Dynatrace

OCTOBER 14, 2021

Gartner defines AIOps as the combination of “big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.” Typically, only the aggregated events will be accessible to ML and will often exclude additional details. What is AIOps?

Artificial Intelligence 246

Artificial Intelligence DevOps Innovation Metrics 246

Dynatrace

JUNE 1, 2020

Artificial intelligence for IT operations (AIOps): AIOps platforms combine big data and machine learning functionality to support IT operations.

Artificial Intelligence 254

Artificial Intelligence Social Media Monitoring IoT 254

Dynatrace

AUGUST 10, 2021

And, they got the chance to do just that at our recent event, DynatraceGo, which I’ll tell you a bit more about in this blog. She dispelled the myth that more big data equals better decisions, higher profits, or more customers. Investing in data is easy but using it is really hard”. DynatraceGo! DynatraceGo!

DevOps 246

DevOps Innovation Big Data Cloud 246

Dynatrace

MARCH 14, 2022

AIOps (or “AI for IT operations”) uses artificial intelligence so that big data can help IT teams work faster and more effectively. There are two main approaches to AIOps: Traditional AIOps: Machine learning models identify correlations between IT events. Gartner introduced the concept of AIOps in 2016.

Healthcare 246

Healthcare Artificial Intelligence Innovation Strategy 246

Dynatrace

AUGUST 14, 2023

These properties reflect the state of the user and apply to all their associated events and event properties. Dynatrace enables organizations to understand user behavior with big data analytics based on gap-free data, eliminating the guesswork involved in understanding the user experience.

Analytics 246

Analytics Social Media Website IoT 246

The Netflix TechBlog

MAY 26, 2020

It is easier to tune a large Spark job for a consistent volume of data. As you may know, S3 can emit messages when events (such as a file creation events) occur which can be directed into an AWS SQS queue. These events represent a specific cut of data from the table.

Network 154

Network Tuning AWS Big Data 154

Dynatrace

DECEMBER 15, 2022

AIOps (artificial intelligence for IT operations) combines big data, AI algorithms, and machine learning for actionable, real-time insights that help ITOps continuously improve operations. ITOps vs. AIOps. The three core components of an AIOps solution are the following: 1. How to modernize ITOps with AIOps.

Artificial Intelligence 246

Artificial Intelligence DevOps Hardware Virtualization 246

The Netflix TechBlog

JUNE 20, 2024

Last but not least, thank you to the organizers of the Data Engineering Open Forum: Chris Colburn , Xinran Waibel , Jai Balani , Rashmi Shamprasad , and Patricia Ho. If you are interested in attending a future Data Engineering Open Forum, we highly recommend you join our Google Group to stay tuned to event announcements.

Data Engineering 206

Data Engineering Engineering Entertainment Software Engineering 206

The Netflix TechBlog

JUNE 7, 2021

Flow Collector consumes two data streams, the IP address change events from Sonar via Kafka and eBPF flow log data from the Flow Exporter sidecars. It performs real time attribution of flow data with application metadata from Sonar. We use Sonar to attribute an IP address to a specific application at a particular time.

Network 327

Network Transportation AWS Cloud 327

Dynatrace

MARCH 24, 2023

Using Marathon, its data center operating system (DC/OS) plugin, Mesos becomes a full container orchestration environment that, like Kubernetes and Docker Swarm, discovers services, balances loads, and manages application containers. Mesos also supports other orchestration engines, including Kubernetes and Docker Swarm.

Infrastructure 246

Infrastructure Open Source Operating System Cloud 246

Dynatrace

FEBRUARY 16, 2023

As teams try to gain insight into this data deluge, they have to balance the need for speed, data fidelity, and scale with capacity constraints and cost. To solve this problem, Dynatrace launched Grail, its causational data lakehouse , in 2022. But logs are just one pillar of the observability triumvirate.

Analytics 246

Analytics Innovation Metrics Database 246

Highly Scalable

MAY 1, 2012

Let us start with a simple example that illustrates capabilities of probabilistic data structures: Let us have a data set that is simply a heap of ten million random integer values and we know that it contains not more than one million distinct values (there are many duplicates). what is the cardinality of the data set)?

Analytics 191

Analytics Traffic Big Data Efficiency 191

The Netflix TechBlog

MAY 4, 2023

In this approach, we record the requests and responses for the service that needs to be updated or replaced to an offline event stream asynchronously. Additionally, for mismatches, we record the normalized and unnormalized responses from both sides to another big data table along with other relevant parameters, such as the diff.

Traffic 347

Traffic Latency Tuning Systems 347

All Things Distributed

DECEMBER 13, 2016

With the launch of the AWS Europe (London) Region, AWS can enable many more UK enterprise, public sector and startup customers to reduce IT costs, address data locality needs, and embark on rapid transformations in critical new areas, such as big data analysis and Internet of Things. Fraud.net is a good example of this.

AWS 154

AWS Cloud Artificial Intelligence IoT 154

The Netflix TechBlog

OCTOBER 18, 2022

by Jun He , Akash Dwivedi , Natallia Dzenisenka , Snehal Chennuru , Praneeth Yenugutala , Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations.

Java 211

Java Scalability Traffic Architecture 211

The Netflix TechBlog

DECEMBER 21, 2020

These principles reduce resource usage by being more efficient and effective while lowering the end-to-end latency in data processing. Both automatic (event-driven) as well as manual (ad-hoc) optimization. It decides what to do and when to do in response to an incoming event. Transparency to end-users.

Storage 212

Storage Latency Efficiency Data Engineering 212

Dynatrace

JUNE 1, 2020

Artificial intelligence for IT operations (AIOps): AIOps platforms combine big data and machine learning functionality to support IT operations.

Monitoring 173

Monitoring Performance Social Media Artificial Intelligence 173

The Morning Paper

OCTOBER 26, 2020

As a production system within Microsoft capturing around a quadrillion events and indexing 16 trillion search keys per day it would be interesting in its own right, but there’s a lot more to it than that. it’s getting much more complex and expensive to process, store, and secure potentially sensitive data.

Cloud 104

Cloud Big Data Latency Architecture 104

The Netflix TechBlog

NOVEMBER 20, 2023

For example, a job would reprocess aggregates for the past 3 days because it assumes that there would be late arriving data, but data prior to 3 days isn’t worth the cost of reprocessing. Backfill: Backfilling datasets is a common operation in big data processing. append, overwrite, etc.).

Processing 219

Processing Big Data Efficiency Engineering 219

Dynatrace

APRIL 11, 2023

The focus on bringing various organizational teams together—such as development, business, and security teams — makes sense as observability data, security data, and business event data coalesce in these cloud-native environments.

Cloud 246

Cloud DevOps Open Source Retail 246

Highly Scalable

MARCH 1, 2012

The idea is to keep all records for one user collocated, so it is possible to fetch such a frame into memory (one user can not produce too many events) and to eliminate site duplicates using hash table or whatever. An alternative technique is to have one entry for one user and append sites to this entry as events arrive.

Database 279

Database Ecommerce Efficiency Engineering 279

Scalegrid

JUNE 28, 2024

This reliability also extends to fault tolerance, as RabbitMQ’s mechanisms ensure that even in the event of a node failure, the message delivery system persists without interruption, safeguarding the system’s overall health and functionality. Can RabbitMQ handle the high-throughput needs of big data applications?

IoT 130

IoT Healthcare Programming Open Source 130

All Things Distributed

SEPTEMBER 29, 2016

In the past we have had Benito Diz, ‎CIO Veolia Water France speak at our events where he has talked about how they have been able to achieve important cost reductions while improving security and agility by moving to AWS. Allez, rendez-vous à Paris – Une nouvelle région AWS arrive en France !

AWS 158

AWS IoT Internet Internet 158

The Morning Paper

MAY 14, 2019

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., For the services under study, Seer has a sweet spot when trained with around 100GB of data and a 100ms sampling interval for measuring queue depths. ASPLOS’19.

Big Data 45

Big Data Cloud Performance Hardware 45

All Things Distributed

SEPTEMBER 26, 2014

We’re at 925 Market Street and our doors will be open 10AM to 6PM on weekdays, with select events running until 8PM on weeknights. Be sure to check the calendar regularly as new evening events will be added regularly. What’s Happening at the AWS Loft. AWS Technical Bootcamps.

AWS 91

AWS Games Education Innovation 91

ScaleOut Software

JULY 19, 2021

And it can maintain contextual information about every data source (like the medical history of a device wearer or the maintenance history of a refrigeration system) and keep it immediately at hand to enhance the analysis.

IoT 78

IoT Big Data Analytics Architecture 78

High Scalability

MARCH 24, 2020

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). 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.

Education 105

Education Software Engineering Engineering Big Data 105