Oda  Shields

Oda Shields


A Real-time Crypto-currency Data Retrieving Application Created -React

Crypto-Currency Data Retriever

A real-time crypto-currency data retrieving application powered by react.

Available Scripts

In the project directory, you can run:

npm start

Runs the app in the development mode.
Open http://localhost:3000 to view it in your browser.

The page will reload when you make changes.
You may also see any lint errors in the console.

npm test

Launches the test runner in the interactive watch mode.
See the section about running tests for more information.

npm run build

Builds the app for production to the build folder.
It correctly bundles React in production mode and optimizes the build for the best performance.

The build is minified and the filenames include the hashes.
Your app is ready to be deployed!

See the section about deployment for more information.

npm run eject

Note: this is a one-way operation. Once you eject, you can't go back!

If you aren't satisfied with the build tool and configuration choices, you can eject at any time. This command will remove the single build dependency from your project.

Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except eject will still work, but they will point to the copied scripts so you can tweak them. At this point you're on your own.

You don't have to ever use eject. The curated feature set is suitable for small and middle deployments, and you shouldn't feel obligated to use this feature. However we understand that this tool wouldn't be useful if you couldn't customize it when you are ready for it.

Learn More

You can learn more in the Create React App documentation.

To learn React, check out the React documentation.

Code Splitting

This section has moved here: https://facebook.github.io/create-react-app/docs/code-splitting

Analyzing the Bundle Size

This section has moved here: https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size

Making a Progressive Web App

This section has moved here: https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app

Advanced Configuration

This section has moved here: https://facebook.github.io/create-react-app/docs/advanced-configuration


This section has moved here: https://facebook.github.io/create-react-app/docs/deployment

npm run build fails to minify

This section has moved here: https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify

Visit the website: https://renisal.github.io/cryptodata-retriever

Author: immonty
Source code: https://github.com/immonty/cryptodata-retriever
License: MIT license
#react #typescript #javascript 

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A Real-time Crypto-currency Data Retrieving Application Created -React
Easter  Deckow

Easter Deckow


PyTumblr: A Python Tumblr API v2 Client



Install via pip:

$ pip install pytumblr

Install from source:

$ git clone https://github.com/tumblr/pytumblr.git
$ cd pytumblr
$ python setup.py install


Create a client

A pytumblr.TumblrRestClient is the object you'll make all of your calls to the Tumblr API through. Creating one is this easy:

client = pytumblr.TumblrRestClient(

client.info() # Grabs the current user information

Two easy ways to get your credentials to are:

  1. The built-in interactive_console.py tool (if you already have a consumer key & secret)
  2. The Tumblr API console at https://api.tumblr.com/console
  3. Get sample login code at https://api.tumblr.com/console/calls/user/info

Supported Methods

User Methods

client.info() # get information about the authenticating user
client.dashboard() # get the dashboard for the authenticating user
client.likes() # get the likes for the authenticating user
client.following() # get the blogs followed by the authenticating user

client.follow('codingjester.tumblr.com') # follow a blog
client.unfollow('codingjester.tumblr.com') # unfollow a blog

client.like(id, reblogkey) # like a post
client.unlike(id, reblogkey) # unlike a post

Blog Methods

client.blog_info(blogName) # get information about a blog
client.posts(blogName, **params) # get posts for a blog
client.avatar(blogName) # get the avatar for a blog
client.blog_likes(blogName) # get the likes on a blog
client.followers(blogName) # get the followers of a blog
client.blog_following(blogName) # get the publicly exposed blogs that [blogName] follows
client.queue(blogName) # get the queue for a given blog
client.submission(blogName) # get the submissions for a given blog

Post Methods

Creating posts

PyTumblr lets you create all of the various types that Tumblr supports. When using these types there are a few defaults that are able to be used with any post type.

The default supported types are described below.

  • state - a string, the state of the post. Supported types are published, draft, queue, private
  • tags - a list, a list of strings that you want tagged on the post. eg: ["testing", "magic", "1"]
  • tweet - a string, the string of the customized tweet you want. eg: "Man I love my mega awesome post!"
  • date - a string, the customized GMT that you want
  • format - a string, the format that your post is in. Support types are html or markdown
  • slug - a string, the slug for the url of the post you want

We'll show examples throughout of these default examples while showcasing all the specific post types.

Creating a photo post

Creating a photo post supports a bunch of different options plus the described default options * caption - a string, the user supplied caption * link - a string, the "click-through" url for the photo * source - a string, the url for the photo you want to use (use this or the data parameter) * data - a list or string, a list of filepaths or a single file path for multipart file upload

#Creates a photo post using a source URL
client.create_photo(blogName, state="published", tags=["testing", "ok"],

#Creates a photo post using a local filepath
client.create_photo(blogName, state="queue", tags=["testing", "ok"],
                    tweet="Woah this is an incredible sweet post [URL]",

#Creates a photoset post using several local filepaths
client.create_photo(blogName, state="draft", tags=["jb is cool"], format="markdown",
                    data=["/Users/johnb/path/to/my/image.jpg", "/Users/johnb/Pictures/kittens.jpg"],
                    caption="## Mega sweet kittens")

Creating a text post

Creating a text post supports the same options as default and just a two other parameters * title - a string, the optional title for the post. Supports markdown or html * body - a string, the body of the of the post. Supports markdown or html

#Creating a text post
client.create_text(blogName, state="published", slug="testing-text-posts", title="Testing", body="testing1 2 3 4")

Creating a quote post

Creating a quote post supports the same options as default and two other parameter * quote - a string, the full text of the qote. Supports markdown or html * source - a string, the cited source. HTML supported

#Creating a quote post
client.create_quote(blogName, state="queue", quote="I am the Walrus", source="Ringo")

Creating a link post

  • title - a string, the title of post that you want. Supports HTML entities.
  • url - a string, the url that you want to create a link post for.
  • description - a string, the desciption of the link that you have
#Create a link post
client.create_link(blogName, title="I like to search things, you should too.", url="https://duckduckgo.com",
                   description="Search is pretty cool when a duck does it.")

Creating a chat post

Creating a chat post supports the same options as default and two other parameters * title - a string, the title of the chat post * conversation - a string, the text of the conversation/chat, with diablog labels (no html)

#Create a chat post
chat = """John: Testing can be fun!
Renee: Testing is tedious and so are you.
John: Aw.
client.create_chat(blogName, title="Renee just doesn't understand.", conversation=chat, tags=["renee", "testing"])

Creating an audio post

Creating an audio post allows for all default options and a has 3 other parameters. The only thing to keep in mind while dealing with audio posts is to make sure that you use the external_url parameter or data. You cannot use both at the same time. * caption - a string, the caption for your post * external_url - a string, the url of the site that hosts the audio file * data - a string, the filepath of the audio file you want to upload to Tumblr

#Creating an audio file
client.create_audio(blogName, caption="Rock out.", data="/Users/johnb/Music/my/new/sweet/album.mp3")

#lets use soundcloud!
client.create_audio(blogName, caption="Mega rock out.", external_url="https://soundcloud.com/skrillex/sets/recess")

Creating a video post

Creating a video post allows for all default options and has three other options. Like the other post types, it has some restrictions. You cannot use the embed and data parameters at the same time. * caption - a string, the caption for your post * embed - a string, the HTML embed code for the video * data - a string, the path of the file you want to upload

#Creating an upload from YouTube
client.create_video(blogName, caption="Jon Snow. Mega ridiculous sword.",

#Creating a video post from local file
client.create_video(blogName, caption="testing", data="/Users/johnb/testing/ok/blah.mov")

Editing a post

Updating a post requires you knowing what type a post you're updating. You'll be able to supply to the post any of the options given above for updates.

client.edit_post(blogName, id=post_id, type="text", title="Updated")
client.edit_post(blogName, id=post_id, type="photo", data="/Users/johnb/mega/awesome.jpg")

Reblogging a Post

Reblogging a post just requires knowing the post id and the reblog key, which is supplied in the JSON of any post object.

client.reblog(blogName, id=125356, reblog_key="reblog_key")

Deleting a post

Deleting just requires that you own the post and have the post id

client.delete_post(blogName, 123456) # Deletes your post :(

A note on tags: When passing tags, as params, please pass them as a list (not a comma-separated string):

client.create_text(blogName, tags=['hello', 'world'], ...)

Getting notes for a post

In order to get the notes for a post, you need to have the post id and the blog that it is on.

data = client.notes(blogName, id='123456')

The results include a timestamp you can use to make future calls.

data = client.notes(blogName, id='123456', before_timestamp=data["_links"]["next"]["query_params"]["before_timestamp"])

Tagged Methods

# get posts with a given tag
client.tagged(tag, **params)

Using the interactive console

This client comes with a nice interactive console to run you through the OAuth process, grab your tokens (and store them for future use).

You'll need pyyaml installed to run it, but then it's just:

$ python interactive-console.py

and away you go! Tokens are stored in ~/.tumblr and are also shared by other Tumblr API clients like the Ruby client.

Running tests

The tests (and coverage reports) are run with nose, like this:

python setup.py test

Author: tumblr
Source Code: https://github.com/tumblr/pytumblr
License: Apache-2.0 license

#python #api 

Ian  Robinson

Ian Robinson


4 Real-Time Data Analytics Predictions for 2021

Data management, analytics, data science, and real-time systems will converge this year enabling new automated and self-learning solutions for real-time business operations.

The global pandemic of 2020 has upended social behaviors and business operations. Working from home is the new normal for many, and technology has accelerated and opened new lines of business. Retail and travel have been hit hard, and tech-savvy companies are reinventing e-commerce and in-store channels to survive and thrive. In biotech, pharma, and healthcare, analytics command centers have become the center of operations, much like network operation centers in transport and logistics during pre-COVID times.

While data management and analytics have been critical to strategy and growth over the last decade, COVID-19 has propelled these functions into the center of business operations. Data science and analytics have become a focal point for business leaders to make critical decisions like how to adapt business in this new order of supply and demand and forecast what lies ahead.

In the next year, I anticipate a convergence of data, analytics, integration, and DevOps to create an environment for rapid development of AI-infused applications to address business challenges and opportunities. We will see a proliferation of API-led microservices developer environments for real-time data integration, and the emergence of data hubs as a bridge between at-rest and in-motion data assets, and event-enabled analytics with deeper collaboration between data scientists, DevOps, and ModelOps developers. From this, an ML engineer persona will emerge.

#analytics #artificial intelligence technologies #big data #big data analysis tools #from our experts #machine learning #real-time decisions #real-time analytics #real-time data #real-time data analytics

Gerhard  Brink

Gerhard Brink


Testing Tools and Considerations for Real-Time Applications - RTInsights

Robust testing means that your Real-Time Application is more stable and reliable than ever before.

When building a Real-Time Application, any engineer would agree that testing the Application is half of the battle. Creating tests that fully cover every scenario is challenging and time-consuming.

Applications are becoming faster and easier to build in new low-code environments. As the Application creation process is revolutionized, the test creation process must be quick to follow; otherwise, the quality of Applications will begin to suffer

Rethinking Test Inputs and Outputs

Low-Code Test Development

Event Mocking

Capture Events and Replay

A New Era of Testing

#application performance #big data #big data analysis tools #big data architectures #big data platforms #real-time decisions #events #low code #real-time data

 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

 iOS App Dev

iOS App Dev


Apache Hudi: How Uber Gets Data a Ride to its Destination

Apache Hudi provides tools to ingest data into HDFS or cloud storage, and is designed to get data into the hands of users and analysts quickly.

At a busy, data-intensive enterprise such as Uber, the volumes of real-time data that need to move through its systems on a minute-by-minute basis reaches epic proportions. This calls for a data lake extraordinaire, in which data can immediately be extracted and leveraged across a range of functions, from back-end business applications to front-end mobile apps. Uber depends on up-to-the-minute bookings and alerts as part of its appeal to customers, so its reliance on real-time data streaming platforms is off-the-charts. It has turned to Apache Hudi, an emerging platform that brings stream processing to big data, providing fresh data while being an order of magnitude efficient over traditional batch processing.

I recently had the opportunity to moderate a webcast about Apache Hudi with Nishith Agarwal and Sivabalan Narayanan, both engineers with Uber. Both Agarwal and Narayanan are active members of the Hudi programming committee.

The Hudi data lake project was originally developed at Uber in 2016, open-sourced in 2017, and submitted to the Apache Incubator in January 2019. Apache Hudi data lake technology enables stream processing on top of Apache Hadoop compatible cloud stores and distributed file systems. The solution provides tools to ingest data onto HDFS or cloud storage, as well as provide an incremental approach to resource-intensive ETL, Hive, or Spark jobs. It is designed to get data into the hands of users and analysts much quicker.

#analytics #big data #big data platforms #data management #expert systems #from our experts #real-time decisions #real-time applications #real-time data