Scalegrid
MAY 13, 2020
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.
Highly Scalable
AUGUST 20, 2013
The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. In recent years, this idea got a lot of traction and a whole bunch of solutions like Twitter’s Storm, Yahoo’s S4, Cloudera’s Impala, Apache Spark, and Apache Tez appeared and joined the army of Big Data and NoSQL systems. The engine can also involve relatively static data (admixtures) loaded from the stores of Aggregated Data.
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The Netflix TechBlog
DECEMBER 21, 2020
By Anupom Syam Background At Netflix, our current data warehouse contains hundreds of Petabytes of data stored in AWS S3 , and each day we ingest and create additional Petabytes. Some of the optimizations are prerequisites for a high-performance data warehouse.
Uber Engineering
OCTOBER 17, 2018
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.
DZone
FEBRUARY 1, 2023
Built on Azure Blob Storage, Azure Data Lake Storage Gen2 is a suite of features for big data analytics. Azure Data Lake Storage Gen1 and Azure Blob Storage's capabilities are combined in Data Lake Storage Gen2. For instance, Data Lake Storage Gen2 offers scale, file-level security, and file system semantics.
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. Containerized data workloads running on Kubernetes offer several advantages over traditional virtual machine/bare metal based data workloads including but not limited to.
Uber Engineering
AUGUST 3, 2018
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.
DZone
JUNE 3, 2019
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. Normally, GPU nodes don't have much room for SSDs, which limits the opportunity to train very deep neural networks that need more data. For example, one well-respected vendor's standard solution is limited to 7.5TB of internal storage, and it can only scale to 30TB.
The Netflix TechBlog
OCTOBER 28, 2021
Data Engineers of Netflix?—?Interview Interview with Pallavi Phadnis This post is part of our “ Data Engineers of Netflix ” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Data Engineers of Netflix?—?Interview
All Things Distributed
AUGUST 18, 2011
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. Many of our Big-Data customers already saw a big drop in their AWS bill last month when the cost of incoming bandwidth was dropped to $0.00. However, this cannot be done without efficient, scalable data analytics.
Uber Engineering
APRIL 5, 2018
Three years ago, Uber Engineering adopted Hadoop as the storage ( HDFS ) and compute ( YARN ) infrastructure for our organization’s big data analysis. This analysis powers our services and enables the delivery of more seamless and reliable user … The post Scaling Uber’s Apache Hadoop Distributed File System for Growth appeared first on Uber Engineering Blog.
DZone
JULY 29, 2019
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. These processes are only possible with a distributed architecture and parallel processing mechanisms that Big Data tools are based on. One of the top trending open-source data storage that responds to most of the use cases is Elasticsearch.
Highly Scalable
MAY 1, 2012
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. Analysis of such large data sets often requires powerful distributed data stores like Hadoop and heavy data processing with techniques like MapReduce. The picture above depicts the fact that this data set basically occupies 40MB of memory (10 million of 4-byte elements).
ScaleOut Software
JULY 19, 2021
Incoming data is saved into data storage (historian database or log store) for query by operational managers who must attempt to find the highest priority issues that require their attention.
The Netflix TechBlog
OCTOBER 15, 2019
Part I: Overview Andreas Andreakis , Falguni Jhaveri , Ioannis Papapanagiotou , Mark Cho , Poorna Reddy , Tongliang Liu Overview It is a commonly observed pattern for applications to utilize multiple datastores where each is used to serve a specific need such as storing the canonical form of data (MySQL etc.), Beyond data synchronization, some applications also need to enrich their data by calling external services.
Highly Scalable
MARCH 1, 2012
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. To explore data modeling techniques, we have to start with a more or less systematic view of NoSQL data models that preferably reveals trends and interconnections.
Uber Engineering
AUGUST 14, 2019
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. Architecture Uber Data Big Data Data Engineering Data Infrastructure data science Data Warehouse Engineering Efficiency
DZone
MAY 30, 2019
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. This has given rise to a completely new set of computing workloads for Machine Learning which drives Artificial Intelligence applications. AI/ML can be applied across a broad spectrum of applications and industries.
All Things Distributed
MAY 18, 2010
Expanding the Cloud - Amazon S3 Reduced Redundancy Storage. Today a new storage option for Amazon S3 has been launched: Amazon S3 Reduced Redundancy Storage (RRS). This new storage option enables customers to reduce their costs by storing non-critical, reproducible data at lower levels of redundancy. This has been an option that customers have been asking us about for some time so we are really pleased to be able to offer this alternative storage option now.
All Things Distributed
AUGUST 20, 2012
Managing Cold Storage with Amazon Glacier. With the introduction of Amazon Glacier , IT organizations now have a solution that removes the headaches of digital archiving and provides extremely low cost storage. Building and managing archive storage that needs to remain operational for decades if not centuries is a major challenge for most organizations. Data is retrieved by scheduling a job, which typically completes within 3 to 5 hours. A Complete Storage Solution.
DZone
FEBRUARY 23, 2023
Introduction With big data streaming platform and event ingestion service Azure Event Hubs , millions of events can be received and processed in a single second. Any real-time analytics provider or batching/storage adaptor can transform and store data supplied to an event hub.
Abhishek Tiwari
DECEMBER 27, 2017
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. A common theme across all these trends is to remove the complexity by simplifying data management as a whole. In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures.
DZone
JANUARY 20, 2023
This, on the one hand, is heavily influenced by the sustained rising and popularity of big-data processing frameworks, including but not limited to Hadoop, Spark, and NoSQL databases; on the other hand, as more and more data are to be analyzed in a correlated and multi-dimensional fashion, it's getting difficult to pack all data into one graph on one instance, having a truly distributed and horizontally scalable graph database is a must-have.
All Things Distributed
FEBRUARY 17, 2011
Since a few days ago this weblog serves 100% of its content directly out of the Amazon Simple Storage Service (S3) without the need for a web server to be involved. Driving Storage Costs Down for AWS Customers. Expanding the Cloud - The AWS Storage Gateway. Driving down the cost of Big-Data analytics. All Things Distributed. Werner Vogels weblog on building scalable and robust distributed systems. New AWS feature: Run your website from Amazon S3.
All Things Distributed
AUGUST 17, 2011
As some of you may remember I was pretty excited when Amazon Simple Storage Service (S3) released its website feature such that I could serve this weblog completely from S3. Amazon S3 is much more than just storage; the network and distributed systems infrastructure to ensure that content can be served fast and at high rates without customers impacting each other, is amazing. Driving Storage Costs Down for AWS Customers. Expanding the Cloud - The AWS Storage Gateway.
Dynatrace
OCTOBER 4, 2022
While data lakes and data warehousing architectures are commonly used modes for storing and analyzing data, a data lakehouse is an efficient third way to store and analyze data that unifies the two architectures while preserving the benefits of both. Data lakes.
Dynatrace
OCTOBER 4, 2022
Several pain points have made it difficult for organizations to manage their data efficiently and create actual value. Limited data availability constrains value creation. Still, it is critical to collect, store, and make easily accessible these massive amounts of log data for analysis.
The Morning Paper
OCTOBER 26, 2020
On the surface this is a paper about fast data ingestion from high-volume streams, with indexing to support efficient querying. Helios also serves as a reference architecture for how Microsoft envisions its next generation of distributed big-data processing systems being built.
All Things Distributed
JULY 7, 2011
AWS Import/Export transfers data off of storage devices using Amazons high-speed internal network and bypassing the Internet. With this new functionality AWS Import/Export now supports importing data directly into Amazon EBS snapshots. Amazon Import/Export is an important tool for customers to accelerate moving large amounts of data into the AWS storage systems. Driving Storage Costs Down for AWS Customers. Expanding the Cloud - The AWS Storage Gateway.
DZone
MAY 21, 2019
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. Many businesses select non-volatile memory express (NVMe) storage when their data-intensive applications demand fast access to data.
Dynatrace
JANUARY 11, 2023
As cloud and big data complexity scales beyond the ability of traditional monitoring tools to handle, next-generation cloud monitoring and observability are becoming necessities for IT teams. Cloud storage monitoring.
Dynatrace
JANUARY 16, 2023
The study analyzes factual Kubernetes production data from thousands of organizations worldwide that are using the Dynatrace Software Intelligence Platform to keep their Kubernetes clusters secure, healthy, and high performing. The data covers the period of January 2021 through September 2022.
The Netflix TechBlog
OCTOBER 27, 2020
By Tianlong Chen and Ioannis Papapanagiotou Netflix has more than 195 million subscribers that generate petabytes of data everyday. The processed data is typically stored as data warehouse tables in AWS S3. Figure 1 shows how we use Bulldozer to move data at Netflix.
Dynatrace
FEBRUARY 16, 2023
How do you get more value from petabytes of exponentially exploding, increasingly heterogeneous data? The short answer: The three pillars of observability—logs, metrics, and traces—converging on a data lakehouse. Logs on Grail Log data is foundational for any IT analytics.
Dynatrace
DECEMBER 15, 2022
Complex cloud computing environments are increasingly replacing traditional data centers. In fact, Gartner estimates that 80% of enterprises will shut down their on-premises data centers by 2025.
Percona
JANUARY 19, 2023
Some startups adopted MySQL in its early days such as Facebook, Uber, Pinterest, and many more, which are now big and successful companies that prove that MySQL can run on large databases and on heavily used sites. There are many compression tools and algorithms for data out there.
All Things Distributed
DECEMBER 13, 2016
The council has deployed IoT Weather Stations in Schools across the City and is using the sensor information collated in a Data Lake to gain insights on whether the weather or pollution plays a part in learning outcomes. With AWS, GoSquared can process tens of billions of data points every day from four continents to provide customers with a single view. Channel 4 (in the UK) chose AWS to help monetize volumes of platform data.
Abhishek Tiwari
DECEMBER 22, 2017
Cheap storage and on-demand compute in the cloud coupled with the emergence of new big data frameworks and tools are forcing us to rethink the whole ETL and data warehousing architecture. Then we perform frequent batch ETL from application databases to a data warehouse. Often post-extraction data is staged in intermediate tables which is followed by transformation and load steps to migrate data into a target database or data warehouse.
All Things Distributed
JUNE 26, 2016
Seamless ingestion of large volumes of sensed data. AdiMap uses Amazon Kinesis to process real-time streaming online ad data and job feeds, and processes them for storage in petabyte-scale Amazon Redshift. Advanced problem solving that connects big data with machine learning. We are excited to offer a complete portfolio of services from our foundational service stack for compute, storage, and networking to our more advanced solutions and applications.
All Things Distributed
OCTOBER 7, 2015
We live in a world where massive volumes of data are generated from websites, connected devices and mobile apps. 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., Big data challenges.
All Things Distributed
JULY 16, 2012
There are sessions in many different categories: Architecture, Big Data, HPC, Computer & Networking, Storage, Databases, Security, Tools & Languages, Media Sharing & Content Delivery, Managing AWS Resources, Enterprise IT, Mobile, Start-up, and more. All Things Distributed. Werner Vogels weblog on building scalable and robust distributed systems. Register for AWS re: Invent. By Werner Vogels on 16 July 2012 09:00 AM. Permalink. Comments ().
All Things Distributed
DECEMBER 8, 2016
AWS data centers in Canada will draw from a regional electricity grid that is 99 percent powered by hydropower. It adopted Amazon Redshift, Amazon EMR and AWS Lambda to power its data warehouse, big data, and data science applications, supporting the development of product features at a fraction of the cost of competing solutions. Earlier this year, Amazon Web Services (AWS) announced it would launch a new AWS infrastructure region in Montreal, Quebec.
ScaleOut Software
APRIL 3, 2020
For example, the parameters for a ventilator could include its identifier, make and model, current location, status (in use, in storage, broken), time in use, technical issues and repairs, and contact information.
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