Lyft Engineering

APRIL 21, 2023

Building a large scale unsupervised model anomaly detection system — Part 1 Distributed Profiling of Model Inference Logs By Anindya Saha , Han Wang , Rajeev Prabhakar Introduction LyftLearn is Lyft’s ML Platform. In part 2, we will focus on how we use this profiled data for anomaly detection. The data is skewed.

Systems 105

Systems Building Machine Learning Datasets 105

Knowledge Hut

OCTOBER 26, 2023

It will regularly scan the operating system files and compare them with a trusted database to detect any unauthorized modifications or tampering. The scanner will compare the patterns in the rulesets with the content of the files, allowing for the detection of malicious files and potentially harmful software. Source code 3.

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Striim

MAY 1, 2024

About UPS Capital UPS Capital, a subsidiary of UPS, specializes in providing financial and insurance solutions tailored to businesses engaged in shipping and logistics. These multifaceted challenges highlighted the urgent need for a sophisticated solution capable of addressing the complexities of modern package delivery and logistics.

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Knowledge Hut

JANUARY 17, 2024

It is usually performed after the cyber threat detection phase, where an automated solution is deployed to look for known threats. Threat hunting goes beyond a regular SIEM (Security information and event management) and EDR (endpoint detection and response) methodology and adds a human intelligence layer. What is an APT?

Certification 52

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Data Engineering Podcast

MARCH 31, 2024

In this episode Maayan Salom explores the approach that she has taken to bring observability, enhanced testing capabilities, and anomaly detection into every step of the dbt developer experience. To bring observability to dbt projects the team at Elementary embedded themselves into the workflow. Your first 30 days are free!

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Pinterest Engineering

OCTOBER 17, 2023

Charles Wu | Software Engineer; Isabel Tallam | Software Engineer; Kapil Bajaj | Engineering Manager Overview In this blog, we present a pragmatic way of integrating analytics, written in Python, with our distributed anomaly detection platform, written in Java. Each job is load-balanced to a node in the Warden cluster. What’s the Goal?

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Algorithm Java Python Software Engineer 87

Knowledge Hut

DECEMBER 28, 2023

Best AI Platforms Let’s look at some of the leading AI platforms that are driving innovative solutions across industries. Data Anomaly Detection: Using ML techniques, identify anomalies in business data and diagnose their fundamental cause. OCI Language is a cloud-based AI solution for large-scale text analysis.

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DataKitchen

NOVEMBER 10, 2023

The key lies in proactively detecting anomalies and notifying responsible parties to implement corrections. Identifying Anomalies: Use advanced algorithms to detect anomalies in data patterns. Develop an automated notification system to alert responsible parties about the detected anomalies.

Data Solutions 56

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Pinterest Engineering

MAY 17, 2023

Isabel Tallam | Sw Eng, Real Time Analytics; Charles Wu | Sw Eng, Real Time Analytics; Kapil Bajaj | Eng Manager, Real Time Analytics Detecting anomalous events has been becoming increasingly important in recent years at Pinterest. In this blog post, we will walk through: The Warden Anomaly Detection Platform. What is Warden?

Algorithm 69

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DataKitchen

MAY 10, 2024

Examples include regular loading of CRM data and anomaly detection. Detecting Anomalies: Identifying irregularities such as embedded delimited data, leading/trailing spaces, inconsistent patterns, and non-standard blank values. Schema Validation: Ensuring the data adheres to the defined schema.

Data Cleanse 51

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Databand.ai

JULY 6, 2023

Data Anomaly: Types, Causes, Detection, and Resolution Helen Soloveichik July 6, 2023 What Is Data Anomaly? A data anomaly, also known as an outlier, is an observation or data point that deviates significantly from the norm, making it inconsistent with the rest of the dataset.

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Cloudera

NOVEMBER 19, 2020

Machine learning algorithms enable fraud detection systems to distinguish between legitimate and fraudulent behaviors. Robust detection-and-response depends on the ability to spot anomalous activity on the system. A solid foundation for fraud detection. A better approach is needed.

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Knowledge Hut

DECEMBER 26, 2023

PaaS PaaS (Platform as a Service) solution uses third-party service providers to deliver hardware and software tools to enterprises over the internet. Actual SaaS solutions are executed from remote locations across the world on cloud servers. Users can install applications from anywhere in the world and use their features.

Cloud Computing 98

Cloud Computing Cloud Amazon Web Services AWS 98

Databand.ai

JULY 6, 2023

This process helps prevent data anomalies, such as duplicate or conflicting entries, which can lead to faulty data analysis. Safeguarding Data Reliability Contextual anomalies are data points that deviate from the norm within a specific context.

Data Integration 52

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Monte Carlo

MARCH 4, 2024

The right data observability tool automatically monitors data and AI pipelines for anomalies and incidents, and then alerts the appropriate stakeholders right away – reducing time to detection and time to resolution. This data pipeline monitoring is crucial for DataOps to function.

Data Pipeline 52

Data Pipeline Data Data Science Software Engineer 52

Towards Data Science

APRIL 6, 2023

Anomaly detection Having a framework for data health is a helpful first step, but it’s critical to be able to turn that framework into code that runs reliably, generates useful alerts, and is easy to configure and maintain. Cost : If you have a small eng team but budget is no barrier, skew towards a third-party solution.

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Monte Carlo

AUGUST 10, 2023

Data observability tools employ automated monitoring, root cause analysis, data lineage, and data health insights to proactively detect, resolve, and prevent data anomalies. You can then multiply this number by your average time-to-detection and average time-to-resolution.

Data Pipeline 98

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Knowledge Hut

FEBRUARY 13, 2024

Given the rate at which new cyber security solutions continue to emerge in response to these risks, it is clear that there will be many new strategies for safeguarding data in the years to come. AI has the potential to improve many aspects of cyber security, including incident response, malware detection, and so on.

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Healthcare Cloud Computing Transportation Manufacturing 98

Monte Carlo

OCTOBER 2, 2023

The data world has certainly benefited tremendously from open source solutions like Spark, dbt, and Airflow, but when it comes to data quality, it’s important to assess the pros and cons of open source data observability tools. Second, open source tools are often more customizable than commercial solutions.

Data Pipeline 52

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Striim

MAY 1, 2024

Understanding Retail Customer Challenges Demand for Personalized Experiences: Today’s consumers are no longer satisfied with one-size-fits-all solutions built on yesterday’s data. Proactive Fraud Detection: Bulletproof Against Risk With the increase in digital transactions comes a heightened risk of fraud.

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Retail Media Cloud Data Integration 52

Knowledge Hut

APRIL 23, 2024

But here's the fascinating part: modern antivirus solutions have expanded their repertoire. They possess the remarkable ability to detect suspicious behaviors exhibited by files and programs, enabling them to uncover potential malware that has yet to be documented in any databases.

Database 52

Database Programming Education Cloud 52

DataKitchen

MAY 10, 2024

It highlights how DataKitchen’s Data Observation solutions equip organizations to enhance their development practices, reduce deployment risks, and increase overall productivity. Examples include regular loading of CRM data and anomaly detection. How Many Tests Ran Per Day? What Is The Average Number Of Tests Per Pipeline?

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Monte Carlo

FEBRUARY 8, 2024

In this post, we will share analyst reports and the core evaluation criteria we see organizations use when ranking data observability solutions. The purpose of data observability tools is to reduce data downtime by automating or accelerating the detection, management and resolution of data quality incidents.

BI 59

BI Data Pipeline Data Warehouse Data 59

Monte Carlo

JANUARY 29, 2024

Monte Carlo supports and extends this native feature by offering Snowflake users a data observability solution to detect, triage, and resolve their data quality issues. Increase data adoption and trust: By catching incidents first, data teams transition from part of the problem to part of the solution. The other half?

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Metadata High Quality Data Data Pipeline Machine Learning 64

Knowledge Hut

APRIL 23, 2024

In this blog post, we'll dive into everything you need to know about RATs – What is Remote Access Trojan (RAT) in cybersecurity, their functionality, risks and dangers, common uses, and targets, as well as how to detect and prevent them from infecting your device.

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Monte Carlo

MARCH 4, 2024

At a broad feature level this translates to machine learning monitors that understand how these aspects of your data platform normally behave and alert you when anomalies occur. These solutions also provide you the data lineage and root cause analysis insights you need to understand how to fix them.

Metadata 52

Metadata Data Lake Data Warehouse Data Engineering 52

Striim

APRIL 1, 2024

Striim Cloud for Application Integration provides a crucial solution to this issue, designed to improve the connectivity and usefulness of these applications by enabling their smooth integration into Google Cloud’s BigQuery. By identifying anomalies or suspicious activities as they happen, businesses can mitigate risks more effectively.

Hospitality 52

Hospitality Transportation Google Cloud Cloud 52

Knowledge Hut

DECEMBER 28, 2023

Cost-Effectiveness: Hadoop is a cost-effective solution compared to traditional data processing systems. Text Sentiment analysis Hadoop source code Big Data Cybersecurity The proposed project entails designing a Hadoop-based solution capable of processing vast volumes of cybersecurity data pertaining to threats and attacks.

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DataKitchen

MAY 10, 2024

This blog explores the third of five critical use cases for Data Observability and Quality Validation—data Production—highlighting how DataKitchen’s Open-Source Data Observability solutions empower organizations to manage this critical stage effectively. Examples include regular loading of CRM data and anomaly detection.

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Raw Data Data Ingestion Datasets Data 40

Monte Carlo

SEPTEMBER 27, 2023

Enter: data observability and data intelligence platforms , the solution to data trust. While data observability solutions like Monte Carlo provide the framework necessary to detect, resolve, and prevent data quality issues, data intelligence platforms like Alation surface these alerts directly to analysts in the catalog itself.

Data Governance 69

Data Governance Government Data Pipeline Data 69

Monte Carlo

NOVEMBER 22, 2022

Despite leveraging a premium data stack and placing expert data leaders at the helm, SeatGeek’s data teams still found themselves losing full days root-causing data anomalies identified by their business users. They found their solution in end-to-end data observability. No manual querying required.

Data 52

Data BI Business Intelligence Data Architecture 52

Monte Carlo

MAY 14, 2024

This setup allows Monte Carlo’s monitors to tap into a rich vein of metadata directly from the tables, making monitoring and anomaly detection more effective and precise. And when issues are detected, notifications need to be automatically directed to the correct owner of the data so that resolution can happen swiftly.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Databand.ai

JULY 5, 2023

Data observability tools are software solutions that oversee, analyze, and improve the performance of data pipelines. By employing these tools, teams can proactively detect issues before they become larger problems that affect business operations.

Data Pipeline 52

Data Pipeline Data Lake Data Warehouse Datasets 52

Snowflake

FEBRUARY 20, 2024

By embracing sustainable practices, telecoms can not only contribute to a greener future but also reap several benefits, including: Revenue growth: Climate change is expected to drive increased demand for telecom services as companies across industries seek to reduce their carbon footprint through remote work, digitization and cloud-based solutions.

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Monte Carlo

NOVEMBER 27, 2023

Well, when you dig deeper into the data downtime survey, a trend starts to emerge: the average time-to-resolution (once detected) for an incident rose from 9 to 15 hours. In our experience, most data teams (perhaps influenced by the common practice of data testing) start the conversation around detection.

Unstructured Data 59

Unstructured Data Data Pipeline Data Banking 59

Monte Carlo

NOVEMBER 28, 2023

Comparison Rules work alongside our machine learning monitors that detect freshness, volume, schema, and data quality anomalies as part of one unified data observability solution. Now, you can detect and resolve these incidents before they irreparably damage operations, revenue, and trust.

Database 59

Database Data Warehouse Machine Learning Data 59

Databand.ai

JULY 10, 2023

Observability Platforms: 8 Key Capabilities and 6 Notable Solutions Eitan Chazbani July 10, 2023 What Is an Observability Platform? An observability platform is a comprehensive solution that allows data engineers to monitor, analyze, and optimize their data pipelines.

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