Thursday, September 2, 2021

RIG stock: 4400 shares | My position | My learning

 
Oil & Gas Stock Roundup Headlined by Equinor's North Sea Start-Up, Marathon's JV





Recap of the Week’s Most-Important Stories

1.  Equinor recently announced that offshore Troll phase 3 production has commenced in the North Sea. The field came online back in 1995. With the initiation of the third phase, the Troll A platform and Kollsnes processing facility’s production life is expected to extend beyond 2050.

Equinor labels the Troll phase 3 as one of its most profitable projects as it has a breakeven price of $10 per barrel. Also, its low carbon dioxide emissions of less than 100 grams per barrel of oil equivalent are praiseworthy. The Zacks Rank #1 (Strong Buy) Norwegian company has managed to reduce emissions from the project as the Troll A platform is powered by electricity from shore.

You can see the complete list of today’s Zacks #1 Rank stocks here.

Phase three will produce from the Troll West gas cap and has a recoverable resource of 347 billion cubic meters of gas. The gas cap is placed over the oil column in Troll West. Oil and gas production from the site will continue simultaneously. The project includes eight wells, divided into two templates, which are connected to the Troll A platform through an umbilical. (Equinor Commences Massive Troll Phase 3 Gas Production)

2.   Downstream operator Marathon Petroleum and Archer-Daniels-Midland Company — a leading producer of food and beverage ingredients — recently entered into an agreement to form a joint venture for producing soybean oil to meet the steadily expanding demand for renewable diesel fuel.

The Spiritwood facility, which is expected to be completed in 2023, will source and process local soybeans and supply the resultant soybean oil solely to Marathon Petroleum. The Spiritwood complex has plans to produce 600 million pounds of refined soybean oil per year, which is enough feedstock for 75 million gallons of sustainable diesel, annually.

The approximately $350-million worth Spiritwood complex will feature cutting-edge automation technologies and have the potential to process 150,000 bushels of soybeans per day once completed. Plenty of new jobs have been created in the region as a result of the new complex building and the facility is said to employ roughly 75 people once it is fully functional.

The Spiritwood complex intends to commence production, keeping the 2023 harvest in view. (Marathon Petroleum, ADM to Form Soybean Oil Production JV)

3.   Offshore contract drilling operator Transocean Ltd. RIG recently secured a firm contract worth $252 million for its new-build ultra-deepwater drillship the Deepwater Atlas from BOE Exploration & Production LLC (BOE), which includes a $30-million mobilization fee. A hefty performance bonus based on the agreed-upon operating indicators is also included in the contract.

This award is the outcome of BOE and the Shenandoah working interest owners' final investment decision to sanction the previously announced Shenandoah project in the United States Gulf of Mexico.

The Shenandoah program is divided into two sections. The Deepwater Atlas is scheduled to begin operations in the third quarter of 2022 after its delivery from the shipyard, initially with dual blowout preventers (BOP) rated to 15,000 psi. The initial drilling operation will take about 255 days to complete and generate nearly $80 million of contract drilling revenues. (Transocean Wins $252M Deal for Deepwater Atlas Drillship)

4.   Canada’s Imperial Oil IMO recently announced that it is on track to build an international-level renewable diesel facility at its Strathcona refinery close to Edmonton, Alberta.

From locally produced and cultivated feedstocks, this new complex is set to produce more than 1 billion liters of renewable diesel fuel, annually. The project is intended to reduce emissions in the Canadian transportation industry by around 3 million tons a year, which is equivalent to removing almost 650,000 passenger vehicles from the road for a year.

Blue hydrogen (hydrogen produced from natural gas with carbon capture and storage) will be used in renewable diesel manufacturing to significantly curb greenhouse gas emissions compared to conventional hydrogen production. Annually, around 500,000 tons of carbon dioxide are predicted to be captured.

To manufacture premium low-carbon diesel fuel, the blue hydrogen and biofeedstock will be mixed with a patented catalyst. (Imperial Oil to Make Renewable Diesel Plant at Strathcona)

5.  NOV Inc. NOV recently inked a deal wherein it agreed to supply two GustoMSCTM NG-20000X self-propelled wind turbine installation jack-up vessel designs known as the Cadeler X-Class to COSCO SHIPPING Heavy Industry and Cadeler.

The Cadeler X-Class has a deck area of 5,600-meter square and a carrying potential of above 17,600 tons. The hybrid, DNV-certified, cyber-secure jack-up vessel is devised to transport and install seven complete 15-Megawatt (MW) turbine sets or five sets of 20-plus MW turbines, representing a major improvement from the previous designs.

The oilfield service provider’s chairman, president and CEO Clay Williams stated that "NOV is honored to partner with Cadeler and COSCO as we design and deliver the next generation of wind turbine installation jack-up vessels. These vessels, which will be a key part of the next stage in the evolution of offshore wind energy, are a perfect example of what comes from close collaboration with our customers and an unending desire to seek improvement." (NOV Inks Deal to Offer Two Self-Propelled Wind Turbine Vessels)

Price Performance

The following table shows the price movement of some major oil and gas players over the past week and during the last six months.

Company    Last Week    Last 6 Months

XOM                   +5.7%             +1.5%
CVX                   +4.6%              -1.6%
COP                  +7.5%              +8.1%
OXY                   +16.9%             -5.1%
SLB                   +9.9%               +1.3%
RIG                    +24.3%             +2.3%
VLO                    +11.5%             -14.2%
MPC                   +9.6%               +7.9%

The Energy Select Sector SPDR — a popular way to track energy companies — was up 7.5% last week. The best performer was Transocean whose stock surged 24.3%.

Over the past six months, the sector tracker has increased 1.2%. Upstream biggie ConocoPhillips COP was the major gainer during the period, experiencing an 8.1% price appreciation.

What’s Next in the Energy World?

As the global oil consumption outlook strengthens amid tightening fundamentals, market participants will be closely tracking the regular releases to watch for signs that could further validate the upward momentum. In this context, the U.S. government’s statistics on oil and natural gas — one of the few solid indicators that come out regularly — will be on energy traders' radar.

Data on rig count from the energy service firm Baker Hughes, which is a pointer to trends in U.S. crude production, is closely followed too. News related to coronavirus vaccine approval/rollout/distribution will be of utmost importance. Last but not the least, investors will keep an eye on the OPEC+ summit outcome for the next course of their oil production policy.


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RIG | 4400 shares | Long term investment



 


Learning Spark: How I plan to finish reading a book in two days?

Sept. 2, 2021

Introduction

I like to talk about my motivation to read books. I like to work on more understanding large distributed system, so I decide to read the book "Learning spark" in two days. It is challenge for me to go over a over 200 pages book in one day, so I like to take two more days.

How I plan to finish reading a book in two days? 

I will add more content here later. 


Everyday I'm Shuffling - Tips for Writing Better Apache Spark Programs

Sept. 2, 2021

Here is the link. 

Want to learn how to write faster and more efficient programs for Apache Spark? Two Spark experts from Databricks, Vida Ha and Holden Karau, provide some performance tuning and testing tips for your Spark applications. Overview: Understanding the Shuffle in Spark - Common causes of inefficiency Understanding when code runs on the drive vs. the workers - Common causes of errors How to factor your code - For reuse between batch and streaming View slides at: http://www.slideshare.net/databricks/... Additional reading: 7 Tips to Debug Apache Spark Code Faster with Databricks https://databricks.com/blog/2016/10/1... Databricks Best Practices and Tips https://docs.databricks.com/user-guid... About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business. Read more here: https://databricks.com/product/unifie... Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data... Instagram: https://www.instagram.com/databricksinc/

UC Berkeley RAD Lab

 Book: Learning Spark 

Spark is an open source project that has been built and is maintained by a thriving and diverse community of developers. If you or your organization are trying Spark for the first time, you might be interested in the history of the project. Spark started in 2009 as a research project in the UC Berkeley RAD Lab, later to become the AMP Lab. The researchers in the lab had previously been working on Hadoop Map‐Reduce, and observed that MapReduce was inefficient for iterative and interactive computing jobs. Thus, from the beginning, Spark was designed to be fast for interactive queries and iterative algorithms, bringing in ideas like support for in-memory storage and efficient fault recovery.

Book reading: Learning Spark | 20 minutes to start

Sept. 2, 2021

Data in all domains is getting bigger. How can you work with it efficiently? This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala.

 

Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.

 

■■ Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell

■■ Leverage Spark’s powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib

■■ Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm

■■ Learn how to deploy interactive, batch, and streaming applications

■■ Connect to data sources including HDFS, Hive, JSON, and S3 

■■ Master advanced topics like data partitioning and shared variables

Holden Karau, a software development engineer at Databricks, is active in open source and the author of Fast Data Processing with Spark (Packt Publishing).

Andy Konwinski, co-founder of Databricks, is a committer on Apache Spark and co-creator of the Apache Mesos project.

Patrick Wendell is a co-founder of Databricks and a committer on Apache Spark. He also maintains several subsystems of Spark’s core engine.

Matei Zaharia, CTO at Databricks, is the creator of Apache Spark and serves as its Vice President at Apache.



Bigtable in action (Google cloud next' 17): CCRi / GeoMesa

Sept. 2, 2021

Here is the link.


40:40/ 55:07 

Anthouny Fox

Director / Lead developer

Spatial and Spatio-temporal data 

AIS - Automatically identified information  - vessel broadcast location every six seconds - satellite picks up

Strava collects up - produces 8 millions a day - fitness - every second - measure 

  • heart rate
  • more ...
Open source project - spatial data - LocationTech GeoMesa 
Apache Kafka - 
LocationTech GeoMesa 
Indexing Spatio-temporal data in Bigtable 
  • Bigtable has a single dimension lexicographic sorted index. 
  • What if we concatenated latitude and longitude?
Indexing Spatio-temporal data in Bigtable using Space-filling curves

We need to encode latitude, longitude, and time into a lexicographically sortable Bigtable key so that data that is nearby in physical space is nearby in index space
  • Space-filling curves project multiple dimensions into a single dimension while preserving locality
  • Hilbert curve - good locality
  • Z-order curve - simple
  • Google S2 - minimizes distortion

Regions translate to range scans - Bigtable index [0, 2^32] 






Wednesday, September 1, 2021

Amazon S3: Software Development Engineer | Sept. 1, 2021

Sept. 1, 2021

Amazon S3, Software Development Engineer

Job ID: 1595765 | Amazon Dev Centre Canada ULC

DESCRIPTION

Amazon Simple Storage Service (S3) is storage for the Internet. Through the use of pioneering techniques in storage & computing, customers can reliably store their on Amazon’s proven computing infrastructure to achieve virtually limitless storage capacity at minimal cost. Amazon S3 provides a simple web services interface that enables customers to store and retrieve any amount of from anywhere in the world. It provides all customers access to the same highly scalable, reliable, secure, fast, inexpensive infrastructure that Amazon uses to run its own global network of websites. The service aims to maximize benefits of scale and to pass those benefits on to the customers.

This position is for the S3 Replication team in Vancouver, Canada. Customers use replication to easily make copies of their S3 objects in AWS regions. The Replication Time Control, feature of the S3 Replication, provides a predictable replication time backed by a Service Level Agreement (SLA). The S3 Replication helps customers meet compliance or business requirements for replication, and provides visibility into the replication process with new Amazon CloudWatch Metrics. You will focus on the challenges of writing software to handle the vast array of S3 use cases with performance being the central binding theme. You will brainstorm new experiences with customers that break ground in enabling new enterprise workloads on S3. You will mentor a group of engineers to build solutions that impact a wide range of customers with a focus on standards in coding, testing, and delivery. The applicant for this role possess solid analytical, and problem-solving skills. The ability to translate technical requirements through all the levels of the software stack is essential. If this sounds like you, come join us and help AWS continue to write the cloud computing story for the industry.

Work-life Balance: Our team works together to provide work/life balance for all team members. We recognize that the circumstances of our team members vary, and we balance work across the team so we’re all able to maintain standards on behalf of our customers, while at the same time allowing for rich and happy personal lives.

On-Call Responsibility: S3 services are highly available, but there are times when we occasionally stray away from our normal operations. To minimize the impact of such excursions, we have on-call rotations. However, we set these up so there are focused time periods when you are on-call and when you are not, so you can focus on your day job when not on-call.

Mentorship & Career Growth: We have a formal mentor search application that lets you find a mentor that works best for you based on location, job family, job level, etc. You can also help you find a mentor or two, because two is better than one. In addition to formal mentors, we work and train together so we are always learning from one another, and we celebrate and support the career progression of our team members.

Inclusive Team Culture: We have a diverse team and drive towards an inclusive culture and work environment. Our team is intentional about attracting, developing, and retaining amazing talent from diverse backgrounds. Our team members are active in Amazon’s 10+ affinity groups, sometimes known as employee resource groups, which bring employees together across businesses and locations around the world. These range from groups such as the Black Employee Network, Amazon Women and Engineering, and LGBTQ+


Amazon is committed to providing accommodations at all stages through recruitment and employment in accordance with applicable human rights and accommodation legislation. If contacted for an employment opportunity, advise Human Resources if you require accommodation, including in order to apply for a position


BASIC QUALIFICATIONS

· 2+ years of non-internship professional software development experience
· Programming experience with at least one modern language such as Java, C++, or C# including object-oriented design
· 1+ years of experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems.
· Bachelor's degree in Computer Science, Computer Engineering or related field.

PREFERRED QUALIFICATIONS

· Strong foundation in , structures, OO and core Computer Science concepts
· Proficiency in, at least, one modern OO programming language such as (preferred), or C++
· Experience with building highly-available and scalable systems
· Understanding of networking protocols
· Understanding of how storage systems work
· Comfortable using environments
· Strong desire to build, sense of ownership, urgency, and drive.
· Demonstrated ability to achieve stretch goals in a highly innovative and fast paced environment
· MS/Phd Degree in Computer Science

Amazon S3: Amazon S3 Update – Strong Read-After-Write Consistency | My 20 minutes study

Amazon S3 Update – Strong Read-After-Write Consistency


When we launched S3 back in 2006, I discussed its virtually unlimited capacity (“…easily store any number of blocks…”), the fact that it was designed to provide 99.99% availability, and that it offered durable storage, with data transparently stored in multiple locations. Since that launch, our customers have used S3 in an amazing diverse set of ways: backup and restore, data archiving, enterprise applications, web sites, big data, and (at last count) over 10,000 data lakes.

One of the more interesting (and sometimes a bit confusing) aspects of S3 and other large-scale distributed systems is commonly known as eventual consistency. In a nutshell, after a call to an S3 API function such as PUT that stores or modifies data, there’s a small time window where the data has been accepted and durably stored, but not yet visible to all GET or LIST requests. Here’s how I see it:

This aspect of S3 can become very challenging for big data workloads (many of which use Amazon EMR) and for data lakes, both of which require access to the most recent data immediately after a write. To help customers run big data workloads in the cloud, Amazon EMR built EMRFS Consistent View and open source Hadoop developers built S3Guard, which provided a layer of strong consistency for these applications.

S3 is Now Strongly Consistent
After that overly-long introduction, I am ready to share some good news!

Effective immediately, all S3 GET, PUT, and LIST operations, as well as operations that change object tags, ACLs, or metadata, are now strongly consistent. What you write is what you will read, and the results of a LIST will be an accurate reflection of what’s in the bucket. This applies to all existing and new S3 objects, works in all regions, and is available to you at no extra charge! There’s no impact on performance, you can update an object hundreds of times per second if you’d like, and there are no global dependencies.

This improvement is great for data lakes, but other types of applications will also benefit. Because S3 now has strong consistency, migration of on-premises workloads and storage to AWS should now be easier than ever before.

We’ve been working with the Amazon EMR team and developers in the open-source community to ensure that customers can take advantage of this update with their big data workloads. As a result of that you no longer need to use EMRFS Consistent View or S3Guard, further reducing the cost to run big data workloads in AWS.

Amazon S3: Amazon S3 Storage Classes | My 20 minutes study

Sept. 1, 2021

Amazon S3 offers a range of storage classes designed for different use cases. These include S3 Standard for general-purpose storage of frequently accessed data; S3 Intelligent-Tiering for data with unknown or changing access patterns; S3 Standard-Infrequent Access (S3 Standard-IA) and S3 One Zone-Infrequent Access (S3 One Zone-IA) for long-lived, but less frequently accessed data; and Amazon S3 Glacier (S3 Glacier) and Amazon S3 Glacier Deep Archive (S3 Glacier Deep Archive) for long-term archive and digital preservation. If you have data residency requirements that can’t be met by an existing AWS Region, you can use the S3 Outposts storage class to store your S3 data on-premises. Amazon S3 also offers capabilities to manage your data throughout its lifecycle. Once an S3 Lifecycle policy is set, your data will automatically transfer to a different storage class without any changes to your application.  

General purpose

Amazon S3 Standard (S3 Standard)

S3 Standard offers high durability, availability, and performance object storage for frequently accessed data. Because it delivers low latency and high throughput, S3 Standard is appropriate for a wide variety of use cases, including cloud applications, dynamic websites, content distribution, mobile and gaming applications, and big data analytics. S3 Storage Classes can be configured at the object level and a single bucket can contain objects stored across S3 Standard, S3 Intelligent-Tiering, S3 Standard-IA, and S3 One Zone-IA. You can also use S3 Lifecycle policies to automatically transition objects between storage classes without any application changes.

Key Features:

  • Low latency and high throughput performance
  • Designed for durability of 99.999999999% of objects across multiple Availability Zones
  • Resilient against events that impact an entire Availability Zone
  • Designed for 99.99% availability over a given year
  • Backed with the Amazon S3 Service Level Agreement for availability
  • Supports SSL for data in transit and encryption of data at rest
  • S3 Lifecycle management for automatic migration of objects to other S3 Storage Classes  

Unknown or changing access

Amazon S3 Intelligent-Tiering (S3 Intelligent-Tiering)

Amazon S3 Intelligent-Tiering (S3 Intelligent-Tiering) is the only cloud storage class that delivers automatic cost savings by moving objects between four access tiers when access patterns change. The S3 Intelligent-Tiering storage class is designed to optimize costs by automatically moving data to the most cost-effective access tier, without operational overhead. It works by storing objects in four access tiers: two low latency access tiers optimized for frequent and infrequent access, and two optional archive access tiers designed for asynchronous access that are optimized for rare access.

S3 Intelligent-Tiering works by storing objects in four access tiers: two low latency access tiers optimized for frequent and infrequent access, and two opt-in archive access tiers designed for asynchronous access that are optimized for rare access. Objects uploaded or transitioned to S3 Intelligent-Tiering are automatically stored in the Frequent Access tier. S3 Intelligent-Tiering works by monitoring access patterns and then moving the objects that have not been accessed in 30 consecutive days to the Infrequent Access tier. Once you have activated one or both of the archive access tiers, S3 Intelligent-Tiering will move objects that haven’t been accessed for 90 consecutive days to the Archive Access tier and then after 180 consecutive days of no access to the Deep Archive Access tier. If the objects are accessed later, S3 Intelligent-Tiering moves the objects back to the Frequent Access tier. If the object you are retrieving is stored in the  Archive or Deep Archive tiers, before you can retrieve the object you must first restore a copy using RestoreObject. For information about restoring archived objects, see Restoring Archived Objects.

There are no retrieval fees when using the S3 Intelligent-Tiering storage class, and no additional tiering fees when objects are moved between access tiers within S3 Intelligent-Tiering. It is the ideal storage class for data sets with unknown storage access patterns, like new applications, or unpredictable access patterns, like data lakes. 

Key Features:

  • Automatically optimizes storage costs for data with changing access patterns
  • Stores objects in four access tiers, optimized for frequent, infrequent, archive, and deep archive access
  • Frequent and Infrequent Access tiers have same low latency and high throughput performance of S3 Standard
  • Activate optional automatic archive capabilities for objects that become rarely accessed
  • Archive access and deep Archive access tiers have same performance as Glacier and Glacier Deep Archive
  • Designed for durability of 99.999999999% of objects across multiple Availability Zones
  • Designed for 99.9% availability over a given year
  • Backed with the Amazon S3 Service Level Agreement for availability
  • Small monthly monitoring and auto-tiering fee
  • No operational overhead, no retrieval fees, no additional tiering fees apply when objects are moved between access tiers within the S3 Intelligent-Tiering storage class

Infrequent access

Amazon S3 Standard-Infrequent Access (S3 Standard-IA)

S3 Standard-IA is for data that is accessed less frequently, but requires rapid access when needed. S3 Standard-IA offers the high durability, high throughput, and low latency of S3 Standard, with a low per GB storage price and per GB retrieval fee. This combination of low cost and high performance make S3 Standard-IA ideal for long-term storage, backups, and as a data store for disaster recovery files. S3 Storage Classes can be configured at the object level and a single bucket can contain objects stored across S3 Standard, S3 Intelligent-Tiering, S3 Standard-IA, and S3 One Zone-IA. You can also use S3 Lifecycle policies to automatically transition objects between storage classes without any application changes.

Key Features:

  • Same low latency and high throughput performance of S3 Standard
  • Designed for durability of 99.999999999% of objects across multiple Availability Zones
  • Resilient against events that impact an entire Availability Zone
  • Data is resilient in the event of one entire Availability Zone destruction
  • Designed for 99.9% availability over a given year
  • Backed with the Amazon S3 Service Level Agreement for availability
  • Supports SSL for data in transit and encryption of data at rest
  • S3 Lifecycle management for automatic migration of objects to other S3 Storage Classes

Amazon S3 One Zone-Infrequent Access (S3 One Zone-IA)

S3 One Zone-IA is for data that is accessed less frequently, but requires rapid access when needed. Unlike other S3 Storage Classes which store data in a minimum of three Availability Zones (AZs), S3 One Zone-IA stores data in a single AZ and costs 20% less than S3 Standard-IA. S3 One Zone-IA is ideal for customers who want a lower-cost option for infrequently accessed data but do not require the availability and resilience of S3 Standard or S3 Standard-IA. It’s a good choice for storing secondary backup copies of on-premises data or easily re-creatable data. You can also use it as cost-effective storage for data that is replicated from another AWS Region using S3 Cross-Region Replication.

S3 One Zone-IA offers the same high durability†, high throughput, and low latency of S3 Standard, with a low per GB storage price and per GB retrieval fee. S3 Storage Classes can be configured at the object level, and a single bucket can contain objects stored across S3 Standard, S3 Intelligent-Tiering, S3 Standard-IA, and S3 One Zone-IA. You can also use S3 Lifecycle policies to automatically transition objects between storage classes without any application changes.

Key Features:

  • Same low latency and high throughput performance of S3 Standard
  • Designed for durability of 99.999999999% of objects in a single Availability Zone†
  • Designed for 99.5% availability over a given year
  • Backed with the Amazon S3 Service Level Agreement for availability
  • Supports SSL for data in transit and encryption of data at rest
  • S3 Lifecycle management for automatic migration of objects to other S3 Storage Classes

† Because S3 One Zone-IA stores data in a single AWS Availability Zone, data stored in this storage class will be lost in the event of Availability Zone destruction.


Archive

Amazon S3 Glacier (S3 Glacier)

S3 Glacier is a secure, durable, and low-cost storage class for data archiving. You can reliably store any amount of data at costs that are competitive with or cheaper than on-premises solutions. To keep costs low yet suitable for varying needs, S3 Glacier provides three retrieval options that range from a few minutes to hours. You can upload objects directly to S3 Glacier, or use S3 Lifecycle policies to transfer data between any of the S3 Storage Classes for active data (S3 Standard, S3 Intelligent-Tiering, S3 Standard-IA, and S3 One Zone-IA) and S3 Glacier. For more information, visit the Amazon S3 Glacier page »

Key Features:

  • Designed for durability of 99.999999999% of objects across multiple Availability Zones
  • Data is resilient in the event of one entire Availability Zone destruction
  • Supports SSL for data in transit and encryption of data at rest
  • Low-cost design is ideal for long-term archive
  • Configurable retrieval times, from minutes to hours
  • S3 PUT API for direct uploads to S3 Glacier, and S3 Lifecycle management for automatic migration of objects

Amazon S3 Glacier Deep Archive (S3 Glacier Deep Archive)

S3 Outposts storage class

Amazon S3 on Outposts delivers object storage to your on-premises AWS Outposts environment. Using the S3 APIs and features available in AWS Regions today, S3 on Outposts makes it easy to store and retrieve data on your Outpost, as well as secure the data, control access, tag, and report on it. S3 on Outposts provides a single Amazon S3 storage class, named S3 Outposts, which uses the S3 APIs, and is designed to durably and redundantly store data across multiple devices and servers on your Outposts. S3 Outposts storage class is ideal for workloads with local data residency requirements, and to satisfy demanding performance needs by keeping data close to on-premises applications.


Key Features:

  • S3 Object compatibility and bucket management through the S3 SDK
  • Designed to durably and redundantly store data on your Outposts
  • Encryption using SSE-S3 and SSE-C
  • Authentication and authorization using IAM, and S3 Access Points
  • Transfer data to AWS Regions using AWS DataSync
  • S3 Lifecycle expiration actions

Performance across the S3 Storage Classes

 S3 StandardS3 Intelligent-Tiering*
S3 Standard-IA
S3 One Zone-IA†
S3 Glacier
S3 Glacier
Deep Archive
Designed for durability
99.999999999%
(11 9’s)
99.999999999%
(11 9’s)
99.999999999%
(11 9’s)
99.999999999%
(11 9’s)
99.999999999%
(11 9’s)
99.999999999%
(11 9’s)
Designed for availability
99.99%99.9%99.9%99.5%99.99%99.99%
Availability SLA99.9%99%99%99%99.9%
99.9%
Availability Zones≥3≥3≥31≥3≥3
Minimum capacity charge per objectN/AN/A128KB128KB40KB40KB
Minimum storage duration chargeN/A30 days30 days30 days90 days180 days
Retrieval feeN/A
N/A
per GB retrieved
per GB retrievedper GB retrievedper GB retrieved
First byte latencymillisecondsmillisecondsmillisecondsmillisecondsselect minutes or hoursselect hours
Storage typeObjectObjectObjectObjectObjectObject
Lifecycle transitionsYesYesYesYesYesYes

† Because S3 One Zone-IA stores data in a single AWS Availability Zone, data stored in this storage class will be lost in the event of Availability Zone destruction.

* S3 Intelligent-Tiering charges a small tiering fee and has a minimum eligible object size of 128KB for auto-tiering. Smaller objects may be stored but will always be charged at the Frequent Access tier rates. See the Amazon S3 Pricing for more information.

** Standard retrievals in archive access tier and deep archive access tier are free. Using the S3 console, you can pay for expedited retrievals if you need faster access to your data from the archive access tiers.

*** S3 Intelligent-Tiering first byte latency for frequent and infrequent access tier is milliseconds access time, and the archive access and deep archive access tiers first byte latency is minutes or hours.