1648113600
Skizze ([ˈskɪt͡sə]: german for sketch) is a sketch data store to deal with all problems around counting and sketching using probabilistic data-structures.
Unlike a Key-Value store, Skizze does not store values, but rather appends values to defined sketches, allowing one to solve frequency and cardinality queries in near O(1) time, with minimal memory footprint.
Current status ==> Alpha (tagged v0.0.2)
Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in 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. This approach often leads to heavyweight high-latency analytical processes and poor applicability to realtime use cases. On the other hand, when one is interested only in simple additive metrics like total page views or average price of conversion, it is obvious that raw data can be efficiently summarized, for example, on a daily basis or using simple in-stream counters. Computation of more advanced metrics like a number of unique visitor or most frequent items is more challenging and requires a lot of resources if implemented straightforwardly.
Skizze is a (fire and forget) service that provides a probabilistic data structures (sketches) storage that allows estimation of these and many other metrics, with a trade off in precision of the estimations for the memory consumption. These data structures can be used both as temporary data accumulators in query processing procedures and, perhaps more important, as a compact – sometimes astonishingly compact – replacement of raw data in stream-based computing.
SELECT count(v) WHERE v >= c1 AND v < c2)
?make dist
./bin/skizze
Two bindings are currently available:
go get github.com/skizzehq/goskizze/skizze
Documentationnpm install --save skizze
DocumentationSkizze comes with a CLI to help test and explore the server. It can be run via
./bin/skizze-cli
Create a new Domain (Collection of Sketches):
#CREATE DOM $name $estCardinality $topk
CREATE DOM demostream 10000000 100
Add values to the domain:
#ADD DOM $name $value1, $value2 ....
ADD DOM demostream zod joker grod zod zod grod
Get the cardinality of the domain:
# GET CARD $name
GET CARD demostream
# returns:
# Cardinality: 9
Get the rankings of the domain:
# GET RANK $name
GET RANK demostream
# returns:
# Rank: 1 Value: zod Hits: 3
# Rank: 2 Value: grod Hits: 2
# Rank: 3 Value: joker Hits: 1
Get the frequencies of values in the domain:
# GET FREQ $name $value1 $value2 ...
GET FREQ demostream zod joker batman grod
# returns
# Value: zod Hits: 3
# Value: joker Hits: 1
# Value: batman Hits: 0
# Value: grod Hits: 2
Get the membership of values in the domain:
# GET MEMB $name $value1 $value2 ...
GET MEMB demostream zod joker batman grod
# returns
# Value: zod Member: true
# Value: joker Member: true
# Value: batman Member: false
# Value: grod Member: true
List all available sketches (created by domains):
LIST
# returns
# Name: demostream Type: CARD
# Name: demostream Type: FREQ
# Name: demostream Type: MEMB
# Name: demostream Type: RANK
Create a new sketch of type $type (CARD, MEMB, FREQ or RANK):
# CREATE CARD $name
CREATE CARD demosketch
Add values to the sketch of type $type (CARD, MEMB, FREQ or RANK):
#ADD $type $name $value1, $value2 ....
ADD CARD demostream zod joker grod zod zod grod
Author: Seiflotfy
Source Code: https://github.com/seiflotfy/skizze
License: MIT License
1620466520
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
1620629020
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.
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
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Traditional data processing application has limitations of its own in terms of processing the large chunk of complex data and this is where the big data processing application comes into play. Big data processing app can easily process complex and large information with their advanced capabilities.
Want to develop a Big Data Processing Application?
WebClues Infotech with its years of experience and serving 350+ clients since our inception is the agency to trust for the Big Data Processing Application development services. With a team that is skilled in the latest technologies, there can be no one better for fulfilling your development requirements.
Want to know more about our Big Data Processing App development services?
Visit: https://www.webcluesinfotech.com/big-data-solutions/
Share your requirements https://www.webcluesinfotech.com/contact-us/
View Portfolio https://www.webcluesinfotech.com/portfolio/
#big data consulting services #big data development experts usa #big data analytics services #big data services #best big data analytics solution provider #big data services and consulting
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Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.
If you think that an entry-level data analyst role might be right for you, you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path?
Let’s take a look at the most important things you need to know.
#data #data-analytics #data-science #data-analysis #big-data-analytics #data-privacy #data-structures #good-company
1624850863
Azure Data Catalog is a Data Catalog cloud service of Microsoft using a crowdsourced approach. It provides an inventory of data used for discovering and understanding the data sources. Microsoft Azure is a Software as a Service (SaaS) application.
“Build Confidence in Azure Data Catalog even having more than millions of accounts”
**Source: **Gartner, Inc
Azure Data Catalog enhances old investments’ performance, adding metadata and notation around the Azure environment’s data. It informs about the Data sources which we have discovered or which we already have. It expresses documentation and describes the schema of the data source. The data source location and a copy of the metadata are present in the Azure Data Catalog. The user can access it easily when needed, and the indexing of metadata helps discover data through a search.### What is Azure Data Catalog?
Azure Data Catalog is a Data Catalog cloud service of Microsoft using a crowdsourced approach. It provides an inventory of data used for discovering and understanding the data sources. Microsoft Azure is a Software as a Service (SaaS) application.
“Build Confidence in Azure Data Catalog even having more than millions of accounts”
**Source: **Gartner, Inc
Azure Data Catalog enhances old investments’ performance, adding metadata and notation around the Azure environment’s data. It informs about the Data sources which we have discovered or which we already have. It expresses documentation and describes the schema of the data source. The data source location and a copy of the metadata are present in the Azure Data Catalog. The user can access it easily when needed, and the indexing of metadata helps discover data through a search.
#big data engineering #blogs #azure data catalog: a quick introduction to data handling service around #azure data catalog #data handling service around #service