What is Greenplum Database? Intro to the Big Data Database


It can scale towards a multi-petabyte level data workload without a single issue, and it allows access to a cluster of powerful servers that will work together within a single SQL interface where you can view all of the data. Polymorphic Data Storage.

In-Stream Big Data Processing

Highly Scalable

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. The engine can also involve relatively static data (admixtures) loaded from the stores of Aggregated Data.

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ScyllaDB Trends – How Users Deploy The Real-Time Big Data Database


ScyllaDB is an open-source distributed NoSQL data store, reimplemented from the popular Apache Cassandra database. ScyllaDB offers significantly lower latency which allows you to process a high volume of data with minimal delay. There are dozens of quality articles on ScyllaDB vs. Cassandra, so we’ll stop short here so we can get to the real purpose of this article, breaking down the ScyllaDB user data.

How Amazon is solving big-data challenges with data lakes

All Things Distributed

Amazon's worldwide financial operations team has the incredible task of tracking all of that data (think petabytes). At Amazon's scale, a miscalculated metric, like cost per unit, or delayed data can have a huge impact (think millions of dollars).

Kubernetes for Big Data Workloads

Abhishek Tiwari

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. Obviously, containerized data workloads have their own challenges.

Performance Monitoring Dashboards in the Age of Big Data Pollution


Big data is like the pollution of the information age. The Big Data Struggle and Performance Reporting. Alternatively, a number of organizations have created their own internal home-grown systems for managing and distilling web performance and monitoring data.

Uber’s Big Data Platform: 100+ Petabytes with Minute Latency

Uber Engineering

To accomplish this, Uber relies heavily on making data-driven decisions at every level, from forecasting rider demand during high traffic events to identifying and addressing bottlenecks … The post Uber’s Big Data Platform: 100+ Petabytes with Minute Latency appeared first on Uber Engineering Blog. Uber is committed to delivering safer and more reliable transportation across our global markets.

Databook: Turning Big Data into Knowledge with Metadata at Uber

Uber Engineering

From driver and rider locations and destinations, to restaurant orders and payment transactions, every interaction on Uber’s transportation platform is driven by data. Data powers Uber’s global marketplace, enabling more reliable and seamless user experiences across our products for riders, … The post Databook: Turning Big Data into Knowledge with Metadata at Uber appeared first on Uber Engineering Blog.

Experiences with approximating queries in Microsoft’s production big-data clusters

The Morning Paper

Experiences with approximating queries in Microsoft’s production big-data clusters Kandula et al., Microsoft’s big data clusters have 10s of thousands of machines, and are used by thousands of users to run some pretty complex queries.

Data Democratization and How to Get Started?


Today data is an important factor for business success. In every business, it has been observed that data is playing a game-changing moment to improve business performance. Data is important and necessary in this increasingly competitive world.

How to Optimize Elasticsearch for Better Search Performance


In today's world, data is generated in high volumes and to make something out of it, extracted data is needed to be transformed, stored, maintained, governed and analyzed. One of the top trending open-source data storage that responds to most of the use cases is Elasticsearch.

Delta: A Data Synchronization and Enrichment Platform

The Netflix TechBlog

Beyond data synchronization, some applications also need to enrich their data by calling external services. Delta is an eventual consistent, event driven, data synchronization and enrichment platform.

Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and…

The Netflix TechBlog

Can I run a check myself to understand what data is behind this metric?” Now, imagine yourself in the role of a software engineer responsible for a micro-service which publishes data consumed by few critical customer facing services (e.g. Design a flexible data model ? —?Represent

Comparing Apache Ignite In-Memory Cache Performance With Hazelcast In-Memory Cache and Java Native Hashmap


This article compares different options for the in-memory maps and their performances in order for an application to move away from traditional RDBMS tables for frequently accessed data. java big data performance apache ignite in-memory data grid in-memory caching distributed cache

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Hyper Scale VPC Flow Logs enrichment to provide Network Insight

The Netflix TechBlog

By collecting, accessing and analyzing network data from a variety of sources like VPC Flow Logs, ELB Access Logs, Custom Exporter Agents, etc, we can provide Network Insight to users through multiple data visualization techniques like Lumen , Atlas , etc.

Benchmarking the AWS Graviton2 with KeyDB


database big data performance benchmarking performance analysis redis alternative ec2 image ec2stack hardware news keydbWe've always been excited about Arm so when Amazon offered us early access to their new Arm-based instances we jumped at the chance to see what they could do.

Driving down the cost of Big-Data analytics - All Things Distributed

All Things Distributed

Driving down the cost of Big-Data analytics. The Amazon Elastic MapReduce (EMR) team announced today the ability to seamlessly use Amazon EC2 Spot Instances with their service, significantly driving down the cost of data analytics in the cloud. All Things Distributed.

Probabilistic Data Structures for Web Analytics and Data Mining

Highly Scalable

Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in the areas like web analytics and Internet advertising. The picture above depicts the fact that this data set basically occupies 40MB of memory (10 million of 4-byte elements).

Advancing Application Performance with NVMe Storage, Part 3


NVMe storage's strong performance, combined with the capacity and data availability benefits of shared NVMe storage over local SSD, makes it a strong solution for AI/ML infrastructures of any size. big data ai data storage ml nvme peperformanceNVMe Storage Use Cases.

Advancing Application Performance With NVMe Storage, Part 2


Using local SSDs inside of the GPU node delivers fast access to data during training, but introduces challenges that impact the overall solution in terms of scalability, data access, and data protection. big data performance data storage ssd nvme gpu ai ml

Scaling Uber’s Apache Hadoop Distributed File System for Growth

Uber Engineering

Three years ago, Uber Engineering adopted Hadoop as the storage ( HDFS ) and compute ( YARN ) infrastructure for our organization’s big data analysis. Architecture Apache Hadoop Big Data Big Data Analysis Garbage Collection Hadoop Hadoop Distributed File System Hadoop Infra & Analytics Team HBase HDFS HDFS Federation HIVE Hoodie Infrastructure JAR Jenkins NameNode PRC Puppet Router-based Federation Uber Uber Data Foundation Team Uber Engineering View File System WebHDFS YARN

Engineering SQL Support on Apache Pinot at Uber

Uber Engineering

Uber leverages real-time analytics on aggregate data to improve the user experience across our products, from fighting fraudulent behavior on Uber Eats to forecasting demand on our platform. .

NoSQL Data Modeling Techniques

Highly Scalable

At the same time, NoSQL data modeling is not so well studied and lacks the systematic theory found in relational databases. In this article I provide a short comparison of NoSQL system families from the data modeling point of view and digest several common modeling techniques.

Uber’s Data Platform in 2019: Transforming Information to Intelligence

Uber Engineering

Architecture Uber Data Big Data Data Analytics Data Platform EoY Infrastructure Product Platform Uber Engineering

Data Mining Problems in Retail

Highly Scalable

Retail is one of the most important business domains for data science and data mining applications because of its prolific data and numerous optimization problems such as optimal prices, discounts, recommendations, and stock levels that can be solved using data analysis methods.

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Less is More: Engineering Data Warehouse Efficiency with Minimalist Design

Uber Engineering

Maintaining Uber’s large-scale data warehouse comes with an operational cost in terms of ETL functions and storage. Once identified, … The post Less is More: Engineering Data Warehouse Efficiency with Minimalist Design appeared first on Uber Engineering Blog.

Productionizing Distributed XGBoost to Train Deep Tree Models with Large Data Sets at Uber

Uber Engineering

Architecture Uber Data Apache Spark Big Data Gradient Boosting Machine Learning Michelangelo ML Uber Engineering XGBoost

MapReduce Patterns, Algorithms, and Use Cases

Highly Scalable

Applications: Log Analysis, Data Querying. Applications: Log Analysis, Data Querying, ETL, Data Validation. Solution: Problem description is split in a set of specifications and specifications are stored as input data for Mappers. Applications: ETL, Data Analysis.

Peloton: Uber’s Unified Resource Scheduler for Diverse Cluster Workloads

Uber Engineering

Cluster management, a common software infrastructure among technology companies, aggregates compute resources from a collection of physical hosts into a shared resource pool, amplifying compute power and allowing for the flexible use of data center hardware. Architecture Apache Hadoop Apache Spark Big Data Capacity Planning Cassandra Cluster Management Data Center Hardware MySQL Peloton Redis Uber Uber Engineering Unified Resource Scheduler Workload Cluster

Fast Intersection of Sorted Lists Using SSE Instructions

Highly Scalable

Big Data Fundamentals Lucene algorithm index information retrieval lucene simd sseIntersection of sorted lists is a cornerstone operation in many applications including search engines and databases because indexes are often implemented using different types of sorted structures.

Peloton: Uber’s Unified Resource Scheduler for Diverse Cluster Workloads

Uber Engineering

Cluster management, a common software infrastructure among technology companies, aggregates compute resources from a collection of physical hosts into a shared resource pool, amplifying compute power and allowing for the flexible use of data center hardware.

5 data integration trends that will define the future of ETL in 2018

Abhishek Tiwari

ETL refers to extract, transform, load and it is generally used for data warehousing and data integration. There are several emerging data trends that will define the future of ETL in 2018. Unified data management architecture. Common in-memory data interfaces.

Moving HPC to the Cloud: A Guide for 2020

High Scalability

This is a guest post by Limor Maayan-Wainstein , a senior technical writer with 10 years of experience writing about cybersecurity, big data, cloud computing, web development, and more.

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When Performance Matters, Think NVMe


The demand for more IT resource-intensive applications has significantly increased today, whether it is to process quicker transactions, gain real-time insight, crunch big data sets, or to meet customer expectations.

Giving data a heartbeat


I love data. I have spent virtually my entire career looking at data. Synthetic data, network data, system data, and the list goes on. As much as I love data, data is cold, it lacks emotion. Dynatrace news.

Advancing Application Performance with NVMe Storage, Part 1


With big data on the rise and data algorithms advancing, the ways in which technology has been applied to real-world challenges have grown more automated and autonomous.

No need to compromise visibility in public clouds with the new Azure services supported by Dynatrace


Our customers have frequently requested support for this first new batch of services, which cover databases, big data, networks, and computing. See the health of your big data resources at a glance. Dynatrace news.

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Ensuring Performance, Efficiency, and Scalability of Digital Transformation

Alex Podelko

Boris has unique expertise in that area – especially in Big Data applications. The CMG Impact conference (February 10-12, 2020 in Las Vegas) is coming. Looking at the program I have the same problem as I always had with CMG conferences – how could I attend all the sessions I want considering that we have multiple tracks?

Python at Netflix

The Netflix TechBlog

Device interaction for the collection of health and other operational data is yet another Python application. We are heavy users of Jupyter Notebooks and nteract to analyze operational data and prototype visualization tools that help us detect capacity regressions.

A guide to Autonomous Performance Optimization


After every experiment run Akamas changes application, runtime, database or cloud configuration based on monitoring data it captured during the previous experiment run. Supported technologies include cloud services, big data, databases, OS, containers, and application runtimes like the JVM.

Tackling the Pipeline Problem in the Architecture Research Community

ACM Sigarch

big-data processing, machine learning, quantum computing, and so on). Computer architecture is an important and exciting field of computer science, which enables many other fields (eg. For those of us who pursued computer architecture as a career, this is well understood.

Top Benefits of Data-Driven Test Automation


If done manually, the process of data-driven testing is really time-consuming. It is usually used for regression testing or with test cases where the same test scripts are run by providing huge test data as input, sequentially. What is data-driven automation testing ?

What is APM?


Trying to manually keep up, configure, script and source data is beyond human capabilities and today everything must be automated and continuous. Artificial intelligence for IT operations (AIOps): AIOps platforms combine big data and machine learning functionality to support IT operations.