#big data tools

[igmpi]

Explore IGMPI’s Big Data Analytics program, designed for professionals seeking expertise in data-driven decision-making. Learn advanced analytics techniques, data mining, machine learning, and business intelligence tools to excel in the fast-evolving world of big data.

[impaaktmagazine]

Navigating the Data World: A Deep Dive into Architecture of Big Data Tools

In today’s digital world, where data has become an integral part of our daily lives. May it be our phone’s microphone, websites, mobile applications, social media, customer feedback, or terms & conditions – we consistently provide “yes” consents, so there is no denying that each individual's data is collected and further pushed to play a bigger role into the decision-making pipeline of the organizations.

This collected data is extracted from different sources, transformed to be used for analytical purposes, and loaded in another location for storage. There are several tools present in the market that could be used for data manipulation. In the next sections, we will delve into some of the top tools used in the market and dissect the information to understand the dynamics of this subject.

Architecture Overview

While researching for top tools, here are a few names that made it to the top of my list – Snowflake, Apache Kafka, Apache Airflow, Tableau, Databricks, Redshift, Bigquery, etc. Let’s dive into their architecture in the following sections:

Snowflake

There are several big data tools in the market serving warehousing purposes for storing structured data and acting as a central repository of preprocessed data for analytics and business intelligence. Snowflake is one of the warehouse solutions. What makes Snowflake different from other solutions is that it is a truly self-managed service, with no hardware requirements and it runs completely on cloud infrastructure making it a go-to for the new Cloud era. Snowflake uses virtual computing instances and a storage service for its computing needs. Understanding the tools' architecture will help us utilize it more efficiently so let’s have a detailed look at the following pointers:

Image credits: Snowflake

Now let’s understand what each layer is responsible for. The Cloud service layer deals with authentication and access control, security, infrastructure management, metadata, and optimizer manager. It is responsible for managing all these features throughout the tool. Query processing is the compute layer where the actual query computation happens and where the cloud compute resources are utilized. Database storage acts as a storage layer for storing the data. 

Considering the fact that there are a plethora of big data tools, we don’t shed significant light on the Apache toolkit, this won’t be justice done to their contribution. We all are familiar with Apache tools being widely used in the Data world, so moving on to our next tool Apache Kafka.

Apache Kafka

Apache Kafka deserves an article in itself due to its prominent usage in the industry. It is a distributed data streaming platform that is based on a publish-subscribe messaging system. Let’s check out Kafka components – Producer and Consumer. Producer is any system that produces messages or events in the form of data for further processing for example web-click data, producing orders in e-commerce, System Logs, etc. Next comes the consumer, consumer is any system that consumes data for example Real-time analytics dashboard, consuming orders in an inventory service, etc.

A broker is an intermediate entity that helps in message exchange between consumer and producer, further brokers have divisions as topic and partition. A topic is a common heading given to represent a similar type of data. There can be multiple topics in a cluster. Partition is part of a topic. Partition is data divided into small sub-parts inside the broker and every partition has an offset.

Another important element in Kafka is the ZooKeeper. A ZooKeeper acts as a cluster management system in Kafka. It is used to store information about the Kafka cluster and details of the consumers. It manages brokers by maintaining a list of consumers. Also, a ZooKeeper is responsible for choosing a leader for the partitions. If any changes like a broker die, new topics, etc., occur, the ZooKeeper sends notifications to Apache Kafka. Zookeeper has a master-slave that handles all the writes, and the rest of the servers are the followers who handle all the reads.

In recent versions of Kafka, it can be used and implemented without Zookeeper too. Furthermore, Apache introduced Kraft which allows Kafka to manage metadata internally without the need for Zookeeper using raft protocol.

Image credits: Emre Akin

Moving on to the next tool on our list, this is another very popular tool from the Apache toolkit, which we will discuss in the next section.

Apache Airflow

Airflow is a workflow management system that is used to author, schedule, orchestrate, and manage data pipelines and workflows. Airflow organizes your workflows as Directed Acyclic Graph (DAG) which contains individual pieces called tasks. The DAG specifies dependencies between task execution and task describing the actual action that needs to be performed in the task for example fetching data from source, transformations, etc.

Airflow has four main components scheduler, DAG file structure, metadata database, and web server. A scheduler is responsible for triggering the task and also submitting the tasks to the executor to run. A web server is a friendly user interface designed to monitor the workflows that let you trigger and debug the behavior of DAGs and tasks, then we have a DAG file structure that is read by the scheduler for extracting information about what task to execute and when to execute them. A metadata database is used to store the state of workflow and tasks. In summary, A workflow is an entire sequence of tasks and DAG with dependencies defined within airflow, a DAG is the actual data structure used to represent tasks. A task represents a single unit of DAG.

As we received brief insights into the top three prominent tools used by the data world, now let’s try to connect the dots and explore the Data story.

Connecting the dots 

To understand the data story, we will be taking the example of a use case implemented at Cubera. Cubera is a big data company based in the USA, India, and UAE. The company is creating a Datalake for data repository to be used for analytical purposes from zero-party data sources as directly from data owners. On an average 100 MB of data per day is sourced from various data sources such as mobile phones, browser extensions, host routers, location data both structured and unstructured, etc. Below is the architecture view of the use case.

Image credits: Cubera

A node js server is built to collect data streams and pass them to the s3 bucket for storage purposes hourly. While the airflow job is to collect data from the s3 bucket and load it further into Snowflake. However, the above architecture was not cost-efficient due to the following reasons:

AWS S3 storage cost (for each hour, typically 1 million files are stored).

Usage costs for ETL running in MWAA (AWS environment).

The managed instance of Apache Airflow (MWAA).

Snowflake warehouse cost.

The data is not real-time, being a drawback.

The risk of back-filling from a sync-point or a failure point in the Apache airflow job functioning.

The idea is to replace this expensive approach with the most suitable one, here we are replacing s3 as a storage option by constructing a data pipeline using Airflow through Kafka to directly dump data to Snowflake. The following is a newfound approach, as Kafka works on the consumer-producer model, snowflake works as a consumer here. The message gets queued on the Kafka topic from the sourcing server. The Kafka for Snowflake connector subscribes to one or more Kafka topics based on the configuration information provided via the Kafka configuration file.

Image credits: Cubera

With around 400 million profile data directly sourced from individual data owners from their personal to household devices as Zero-party data, 2nd Party data from various app partnerships, Cubera Data Lake is continually being refined.

Conclusion

With so many tools available in the market, choosing the right tool is a task. A lot of factors should be taken into consideration before making the right decision, these are some of the factors that will help you in the decision-making – Understanding the data characteristics like what is the volume of data, what type of data we are dealing with - such as structured, unstructured, etc. Anticipating the performance and scalability needs, budget, integration requirements, security, etc.

This is a tedious process and no single tool can fulfill all your data requirements but their desired functionalities can make you lean towards them. As noted earlier, in the above use case budget was a constraint so we moved from the s3 bucket to creating a data pipeline in Airflow. There is no wrong or right answer to which tool is best suited. If we ask the right questions, the tool should give you all the answers.     

Join the conversation on IMPAAKT! Share your insights on big data tools and their impact on businesses. Your perspective matters—get involved today!                 

[screambirdscreaming]

I remember when 3D printers first became a thing and there was a huuuge hype about how you could 3D print anything and it would revolutionize everything

And then there was a phase of fussing around realizing that there are actually some unintuitive constraints on what shapes are printable based on slicing and support of overhangs, and how you have to do fiddly business like putting tape on the platform and watching the first couple layers like a hawk or it can detach and slump sideways and become a big horrible useless mess

And then, after all that, people kinda came around to realize that even if you get all that sorted, the object you've made is fundamentally an object made of moderately-well-adhered layers of brittle plastic, which is actually a pretty shitty material for almost every purpose.

And aside from a few particular use cases the whole hype just sort of dropped off to nothing.

Anyway I feel like we're seeing pretty much the same arc play out with generative AI.

[ceramicbeetle]

ACTUALLY,,, hm the Doctor might be a better role to sub hawkeye in for due to the whole “is treated like a tool and isn’t given any Choice about being present on the ship” thing that makes it analogous to hawkeye being drafted. and the doctor is a character who attempts to insist on his humanity but is often dismissed and treated like a tool instead — or at least like an inconvenience (thinking about the thesis paper i read about how the Doctor’s situation can be read as a disability allegory for how characters treat him and his hologram emitters as an inconvenient accommodation/mobility aid that can be stolen/taken as punishment/dismissed as frivolous at the whim of the allegorically Abled members of the crew)

[truetechreview]

How DeepSeek AI Revolutionizes Data Analysis

1. Introduction: The Data Analysis Crisis and AI’s Role2. What Is DeepSeek AI?3. Key Features of DeepSeek AI for Data Analysis4. How DeepSeek AI Outperforms Traditional Tools5. Real-World Applications Across Industries6. Step-by-Step: Implementing DeepSeek AI in Your Workflow7. FAQs About DeepSeek AI8. Conclusion 1. Introduction: The Data Analysis Crisis and AI’s Role Businesses today generate…

[newfangled-vady]

Top 5 Benefits of Low-Code/No-Code BI Solutions

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[ebubekiratabey]

Hello, you know there are a lot of different AI Tools to make our life easier. I want to share them with. The first one is free AI Animation Tools for 3D Masterpieces. You will find 6 different AI Tools.

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Ebubekir ATABEY

Data Scientist

[superconductivebean]

I know what AI is, and this is my main reason not to use it.

When AI receives a prompt, it selects a *token*—the key word for its search query. It does not open up Google for you—it selects a group of tokens, interlinked with the OG token, to generate the probability matrix for its eventual first word.

AI will generate many more of these matrixes, but the key is: 1) its choice for words is dictated by the Perplexity (how wordly; rarer words will be more likely to be chosen from the matrix); 2) its sentence structure and the overall use of tokens is determined by the Burstiness (the length and the complexity of the sentence, and how many more tokens can be allowed per answer); 3) the matrix itself—the process of generating them rather than a standalone among the many—is set by the OG token, so AI is already limited in its output from the very beginning. AI cannot have unlimited access to tokens; tokens are not only the unit of memory—they require computation power, so any given AI will be limited in how much of them it can have, otherwise it is limited by the hardware.

How is AI able co computate all of that? Datasets, unethically gathered—stolen, in short; the fact that AI consume their own outputs after the web has been scrapped goes without mention.

It's a simplified explanation, but it's enough to say such a limited, crude, purely mechanical software can only be used for style checking or assigning a reading grade—something you would need math for. Whoever decided to market it for *creativity* has spawned NFT 2.0 Idiot Bubble.

I hope we are not seeing regurgitated Project Gutenberg for 19th Century Authentic Dark Academia Dark Fantasy Writing bullshite after it eventually bursts.

anyway I saw the AI poll and thought it needed like, more options? anyhow. I would like to hear nuances in the reblogs!

I guess if you do use AI just straight up unfollow my blog?

[webscraping82]

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[modernadesigns]

Geld verdienen met kunstmatige intelligentie

[fire-on-fuel]

hyperspecific complaint but I really dislike when writers/authors/showrunners/artists/directors who are fans of a franchise start working for them and have no idea how to do their job. It's very easy to tell when someone thinks being a lifelong fan is a free pass to treat their job like theyre playing in a fandom sandbox. Once you take on the responsibility to add to an IP there's a certain level of respect for both the overarching narrative, your fellow creators, existing work, and media cohesion that should be standard. If you're hired to work on a permanent fixture to a storyline or especially a complicated expanded universe, who you are creating for and what will be affected as collateral is key. Yeah sometimes it is egregious and authors are killing off eachothers characters and canceling out eachother's lore but sometimes it's as minor as a show taking inspiration and characters from another media work in the universe and causing issues with storylines/timelines purely because they see that other work as a thing to ref off of and discard back to obscurity and not something their own work is improved by aligning with or on the same standing as

[curiousquill1]

How Portfolio Management Firms Use Advanced Data Analytics to Transform Investment Strategies

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[justnshalom]

Efficient Tools for Working with Large Messages

Introduction Working with large messages can be a daunting task, especially when it comes to optimizing performance and improving data processing capabilities. In this article, we will explore some of the best tools available that can assist you in effectively working with large messages. 1. Apache Kafka One of the most popular tools for handling large messages is Apache Kafka. It is a scalable,…

[classroomlearning]

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[herovired12]

Top Big Data Analytics tools are essential for processing and analyzing vast amounts of data to uncover insights and trends. They facilitate data visualization, predictive analytics, and real-time processing. Popular tools include Apache Hadoop, Spark, Tableau, and SAS, each offering unique features to handle complex datasets and support business decision-making. Click here to learn more.