Reid  Rohan

Reid Rohan

1657651920

Suckit: Suck Up A Data Stream and Store It in LevelDB

Suckit

Suck up a data stream and store it in LevelDB.

Overview

Suckit exposes LevelDB (via level-store) over HTTP. It's really that simple.

Super Quickstart

In one terminal:

➜  suckit git:(master) mkdir data
➜  suckit git:(master) suckit 3000 ./data
["2013-05-16T02:53:33.537Z","INFO","started",{"session":"1368672813532-41998","dataPath":"/Users/conrad/work/seriousbusiness/suckit/data","port":3000}]
["2013-05-16T02:53:59.619Z","INFO","request",{"session":"1368672813532-41998","request":"1368672839619-66909","method":"POST","url":"/my-bucket/my-file"}]
["2013-05-16T02:53:59.622Z","INFO","opening bucket",{"session":"1368672813532-41998","name":"my-bucket"}]
["2013-05-16T02:53:59.624Z","INFO","asked to write to file",{"session":"1368672813532-41998","request":"1368672839619-66909","bucket":"my-bucket","file":"my-file","append":true}]
["2013-05-16T02:53:59.712Z","INFO","beginning to write to file",{"session":"1368672813532-41998","request":"1368672839619-66909","bucket":"my-bucket","file":"my-file","append":true,"newFile":true}]
["2013-05-16T02:53:59.716Z","INFO","finished writing to file",{"session":"1368672813532-41998","request":"1368672839619-66909","bucket":"my-bucket","file":"my-file","append":true,"newFile":true}]

In another:

➜  suckit git:(master) curl -v -X POST http://127.0.0.1:3000/my-bucket/my-file -d 'this is some content!'
* About to connect() to 127.0.0.1 port 3000 (#0)
*   Trying 127.0.0.1... connected
* Connected to 127.0.0.1 (127.0.0.1) port 3000 (#0)
> POST /my-bucket/my-file HTTP/1.1
> User-Agent: curl/7.21.4 (universal-apple-darwin11.0) libcurl/7.21.4 OpenSSL/0.9.8r zlib/1.2.5
> Host: 127.0.0.1:3000
> Accept: */*
> Content-Length: 21
> Content-Type: application/x-www-form-urlencoded
>
< HTTP/1.1 201 Created
< location: /my-bucket/my-file
< Date: Thu, 16 May 2013 02:53:59 GMT
< Connection: keep-alive
< Transfer-Encoding: chunked
<
* Connection #0 to host 127.0.0.1 left intact
* Closing connection #0

Installation

Available via npm:

$ npm install suckit -g

Or via git:

$ npm install git://github.com/deoxxa/suckit.git -g

Author: deoxxa
Source Code: https://github.com/deoxxa/suckit 
License: View license

#javascript #leveldb #data #stream #node 

What is GEEK

Buddha Community

Suckit: Suck Up A Data Stream and Store It in LevelDB
 iOS App Dev

iOS App Dev

1620466520

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

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Gerhard  Brink

Gerhard Brink

1622108520

Stateful stream processing with Apache Flink(part 1): An introduction

Apache Flink, a 4th generation Big Data processing framework provides robust **stateful stream processing capabilitie**s. So, in a few parts of the blogs, we will learn what is Stateful stream processing. And how we can use Flink to write a stateful streaming application.

What is stateful stream processing?

In general, stateful stream processing is an application design pattern for processing an unbounded stream of events. Stateful stream processing means a** “State”** is shared between events(stream entities). And therefore past events can influence the way the current events are processed.

Let’s try to understand it with a real-world scenario. Suppose we have a system that is responsible for generating a report. It comprising the total number of vehicles passed from a toll Plaza per hour/day. To achieve it, we will save the count of the vehicles passed from the toll plaza within one hour. That count will be used to accumulate it with the further next hour’s count to find the total number of vehicles passed from toll Plaza within 24 hours. Here we are saving or storing a count and it is nothing but the “State” of the application.

Might be it seems very simple, but in a distributed system it is very hard to achieve stateful stream processing. Stateful stream processing is much more difficult to scale up because we need different workers to share the state. Flink does provide ease of use, high efficiency, and high reliability for the**_ state management_** in a distributed environment.

#apache flink #big data and fast data #flink #streaming #streaming solutions ##apache flink #big data analytics #fast data analytics #flink streaming #stateful streaming #streaming analytics

Reid  Rohan

Reid Rohan

1657651920

Suckit: Suck Up A Data Stream and Store It in LevelDB

Suckit

Suck up a data stream and store it in LevelDB.

Overview

Suckit exposes LevelDB (via level-store) over HTTP. It's really that simple.

Super Quickstart

In one terminal:

➜  suckit git:(master) mkdir data
➜  suckit git:(master) suckit 3000 ./data
["2013-05-16T02:53:33.537Z","INFO","started",{"session":"1368672813532-41998","dataPath":"/Users/conrad/work/seriousbusiness/suckit/data","port":3000}]
["2013-05-16T02:53:59.619Z","INFO","request",{"session":"1368672813532-41998","request":"1368672839619-66909","method":"POST","url":"/my-bucket/my-file"}]
["2013-05-16T02:53:59.622Z","INFO","opening bucket",{"session":"1368672813532-41998","name":"my-bucket"}]
["2013-05-16T02:53:59.624Z","INFO","asked to write to file",{"session":"1368672813532-41998","request":"1368672839619-66909","bucket":"my-bucket","file":"my-file","append":true}]
["2013-05-16T02:53:59.712Z","INFO","beginning to write to file",{"session":"1368672813532-41998","request":"1368672839619-66909","bucket":"my-bucket","file":"my-file","append":true,"newFile":true}]
["2013-05-16T02:53:59.716Z","INFO","finished writing to file",{"session":"1368672813532-41998","request":"1368672839619-66909","bucket":"my-bucket","file":"my-file","append":true,"newFile":true}]

In another:

➜  suckit git:(master) curl -v -X POST http://127.0.0.1:3000/my-bucket/my-file -d 'this is some content!'
* About to connect() to 127.0.0.1 port 3000 (#0)
*   Trying 127.0.0.1... connected
* Connected to 127.0.0.1 (127.0.0.1) port 3000 (#0)
> POST /my-bucket/my-file HTTP/1.1
> User-Agent: curl/7.21.4 (universal-apple-darwin11.0) libcurl/7.21.4 OpenSSL/0.9.8r zlib/1.2.5
> Host: 127.0.0.1:3000
> Accept: */*
> Content-Length: 21
> Content-Type: application/x-www-form-urlencoded
>
< HTTP/1.1 201 Created
< location: /my-bucket/my-file
< Date: Thu, 16 May 2013 02:53:59 GMT
< Connection: keep-alive
< Transfer-Encoding: chunked
<
* Connection #0 to host 127.0.0.1 left intact
* Closing connection #0

Installation

Available via npm:

$ npm install suckit -g

Or via git:

$ npm install git://github.com/deoxxa/suckit.git -g

Author: deoxxa
Source Code: https://github.com/deoxxa/suckit 
License: View license

#javascript #leveldb #data #stream #node 

Cyrus  Kreiger

Cyrus Kreiger

1618039260

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt