#data pipeline

[analyticspursuit]

What is a Data Pipeline? | Data Pipeline Explained in 60 Seconds

If you've been curious about data pipelines but don't know what they are, this video is for you! Data pipelines are a powerful way to manage and process data, and in this video, we'll explain them in 60 seconds.

If you're looking to learn more about data pipelines, or want to know what they are used for, then this video is for you! We'll walk you through the data pipeline architecture and share some of the uses cases for data pipelines.

By the end of this video, you'll have a better understanding of what a data pipeline is and how it can help you with your data management needs!

[lisakeller22]

Creating A Successful Data Pipeline: An In-Depth Guide

Explore the blog to learn how to develop a data pipeline for effective data management. Discover the key components, best practices, applications, hurdles, and solutions to streamline processes.

[jcmarchi]

Charity Majors, CTO & Co-Founder at Honeycomb – Interview Series

New Post has been published on https://thedigitalinsider.com/charity-majors-cto-co-founder-at-honeycomb-interview-series/

Charity Majors, CTO & Co-Founder at Honeycomb – Interview Series

Charity is an ops engineer and accidental startup founder at Honeycomb. Before this she worked at Parse, Facebook, and Linden Lab on infrastructure and developer tools, and always seemed to wind up running the databases. She is the co-author of O’Reilly’s Database Reliability Engineering, and loves free speech, free software, and single malt scotch.

You were the Production Engineering Manager at Facebook (Now Meta) for over 2 years, what were some of your highlights from this period and what are some of your key takeaways from this experience?

I worked on Parse, which was a backend for mobile apps, sort of like Heroku for mobile. I had never been interested in working at a big company, but we were acquired by Facebook. One of my key takeaways was that acquisitions are really, really hard, even in the very best of circumstances. The advice I always give other founders now is this: if you’re going to be acquired, make sure you have an executive sponsor, and think really hard about whether you have strategic alignment. Facebook acquired Instagram not long before acquiring Parse, and the Instagram acquisition was hardly bells and roses, but it was ultimately very successful because they did have strategic alignment and a strong sponsor.

I didn’t have an easy time at Facebook, but I am very grateful for the time I spent there; I don’t know that I could have started a company without the lessons I learned about organizational structure, management, strategy, etc. It also lent me a pedigree that made me attractive to VCs, none of whom had given me the time of day until that point. I’m a little cranky about this, but I’ll still take it.

Could you share the genesis story behind launching Honeycomb?

Definitely. From an architectural perspective, Parse was ahead of its time — we were using microservices before there were microservices, we had a massively sharded data layer, and as a platform serving over a million mobile apps, we had a lot of really complicated multi-tenancy problems. Our customers were developers, and they were constantly writing and uploading arbitrary code snippets and new queries of, shall we say, “varying quality” — and we just had to take it all in and make it work, somehow.

We were on the vanguard of a bunch of changes that have since gone mainstream. It used to be that most architectures were pretty simple, and they would fail repeatedly in predictable ways. You typically had a web layer, an application, and a database, and most of the complexity was bound up in your application code. So you would write monitoring checks to watch for those failures, and construct static dashboards for your metrics and monitoring data.

This industry has seen an explosion in architectural complexity over the past 10 years. We blew up the monolith, so now you have anywhere from several services to thousands of application microservices. Polyglot persistence is the norm; instead of “the database” it’s normal to have many different storage types as well as horizontal sharding, layers of caching, db-per-microservice, queueing, and more. On top of that you’ve got server-side hosted containers, third-party services and platforms, serverless code, block storage, and more.

The hard part used to be debugging your code; now, the hard part is figuring out where in the system the code is that you need to debug. Instead of failing repeatedly in predictable ways, it’s more likely the case that every single time you get paged, it’s about something you’ve never seen before and may never see again.

That’s the state we were in at Parse, on Facebook. Every day the entire platform was going down, and every time it was something different and new; a different app hitting the top 10 on iTunes, a different developer uploading a bad query.

Debugging these problems from scratch is insanely hard. With logs and metrics, you basically have to know what you’re looking for before you can find it. But we started feeding some data sets into a FB tool called Scuba, which let us slice and dice on arbitrary dimensions and high cardinality data in real time, and the amount of time it took us to identify and resolve these problems from scratch dropped like a rock, like from hours to…minutes? seconds? It wasn’t even an engineering problem anymore, it was a support problem. You could just follow the trail of breadcrumbs to the answer every time, clicky click click.

It was mind-blowing. This massive source of uncertainty and toil and unhappy customers and 2 am pages just … went away. It wasn’t until Christine and I left Facebook that it dawned on us just how much it had transformed the way we interacted with software. The idea of going back to the bad old days of monitoring checks and dashboards was just unthinkable.

But at the time, we honestly thought this was going to be a niche solution — that it solved a problem other massive multitenant platforms might have. It wasn’t until we had been building for almost a year that we started to realize that, oh wow, this is actually becoming an everyone problem.

For readers who are unfamiliar, what specifically is an observability platform and how does it differ from traditional monitoring and metrics?

Traditional monitoring famously has three pillars: metrics, logs and traces. You usually need to buy many tools to get your needs met: logging, tracing, APM, RUM, dashboarding, visualization, etc. Each of these is optimized for a different use case in a different format. As an engineer, you sit in the middle of these, trying to make sense of all of them. You skim through dashboards looking for visual patterns, you copy-paste IDs around from logs to traces and back. It’s very reactive and piecemeal, and typically you refer to these tools when you have a problem — they’re designed to help you operate your code and find bugs and errors.

Modern observability has a single source of truth; arbitrarily wide structured log events. From these events you can derive your metrics, dashboards, and logs. You can visualize them over time as a trace, you can slice and dice, you can zoom in to individual requests and out to the long view. Because everything’s connected, you don’t have to jump around from tool to tool, guessing or relying on intuition. Modern observability isn’t just about how you operate your systems, it’s about how you develop your code. It’s the substrate that allows you to hook up powerful, tight feedback loops that help you ship lots of value to users swiftly, with confidence, and find problems before your users do.

You’re known for believing that observability offers a single source of truth in engineering environments. How does AI integrate into this vision, and what are its benefits and challenges in this context?

Observability is like putting your glasses on before you go hurtling down the freeway. Test-driven development (TDD) revolutionized software in the early 2000s, but TDD has been losing efficacy the more complexity is located in our systems instead of just our software. Increasingly, if you want to get the benefits associated with TDD, you actually need to instrument your code and perform something akin to observability-driven development, or ODD, where you instrument as you go, deploy fast, then look at your code in production through the lens of the instrumentation you just wrote and ask yourself: “is it doing what I expected it to do, and does anything else look … weird?”

Tests alone aren’t enough to confirm that your code is doing what it’s supposed to do. You don’t know that until you’ve watched it bake in production, with real users on real infrastructure.

This kind of development — that includes production in fast feedback loops — is (somewhat counterintuitively) much faster, easier and simpler than relying on tests and slower deploy cycles. Once developers have tried working that way, they’re famously unwilling to go back to the slow, old way of doing things.

What excites me about AI is that when you’re developing with LLMs, you have to develop in production. The only way you can derive a set of tests is by first validating your code in production and working backwards. I think that writing software backed by LLMs will be as common a skill as writing software backed by MySQL or Postgres in a few years, and my hope is that this drags engineers kicking and screaming into a better way of life.

You’ve raised concerns about mounting technical debt due to the AI revolution. Could you elaborate on the types of technical debts AI can introduce and how Honeycomb helps in managing or mitigating these debts?

I’m concerned about both technical debt and, perhaps more importantly, organizational debt. One of the worst kinds of tech debt is when you have software that isn’t well understood by anyone. Which means that any time you have to extend or change that code, or debug or fix it, somebody has to do the hard work of learning it.

And if you put code into production that nobody understands, there’s a very good chance that it wasn’t written to be understandable. Good code is written to be easy to read and understand and extend. It uses conventions and patterns, it uses consistent naming and modularization, it strikes a balance between DRY and other considerations. The quality of code is inseparable from how easy it is for people to interact with it. If we just start tossing code into production because it compiles or passes tests, we’re creating a massive iceberg of future technical problems for ourselves.

If you’ve decided to ship code that nobody understands, Honeycomb can’t help with that. But if you do care about shipping clean, iterable software, instrumentation and observability are absolutely essential to that effort. Instrumentation is like documentation plus real-time state reporting. Instrumentation is the only way you can truly confirm that your software is doing what you expect it to do, and behaving the way your users expect it to behave.

How does Honeycomb utilize AI to improve the efficiency and effectiveness of engineering teams?

Our engineers use AI a lot internally, especially CoPilot. Our more junior engineers report using ChatGPT every day to answer questions and help them understand the software they’re building. Our more senior engineers say it’s great for generating software that would be very tedious or annoying to write, like when you have a giant YAML file to fill out. It’s also useful for generating snippets of code in languages you don’t usually use, or from API documentation. Like, you can generate some really great, usable examples of stuff using the AWS SDKs and APIs, since it was trained on repos that have real usage of that code.

However, any time you let AI generate your code, you have to step through it line by line to ensure it’s doing the right thing, because it absolutely will hallucinate garbage on the regular.

Could you provide examples of how AI-powered features like your query assistant or Slack integration enhance team collaboration?

Yeah, for sure. Our query assistant is a great example. Using query builders is complicated and hard, even for power users. If you have hundreds or thousands of dimensions in your telemetry, you can’t always remember offhand what the most valuable ones are called. And even power users forget the details of how to generate certain kinds of graphs.

So our query assistant lets you ask questions using natural language. Like, “what are the slowest endpoints?”, or “what happened after my last deploy?” and it generates a query and drops you into it. Most people find it difficult to compose a new query from scratch and easy to tweak an existing one, so it gives you a leg up.

Honeycomb promises faster resolution of incidents. Can you describe how the integration of logs, metrics, and traces into a unified data type aids in quicker debugging and problem resolution?

Everything is connected. You don’t have to guess. Instead of eyeballing that this dashboard looks like it’s the same shape as that dashboard, or guessing that this spike in your metrics must be the same as this spike in your logs based on time stamps….instead, the data is all connected. You don’t have to guess, you can just ask.

Data is made valuable by context. The last generation of tooling worked by stripping away all of the context at write time; once you’ve discarded the context, you can never get it back again.

Also: with logs and metrics, you have to know what you’re looking for before you can find it. That’s not true of modern observability. You don’t have to know anything, or search for anything.

When you’re storing this rich contextual data, you can do things with it that feel like magic. We have a tool called BubbleUp, where you can draw a bubble around anything you think is weird or might be interesting, and we compute all the dimensions inside the bubble vs outside the bubble, the baseline, and sort and diff them. So you’re like “this bubble is weird” and we immediately tell you, “it’s different in xyz ways”. SO much of debugging boils down to “here’s a thing I care about, but why do I care about it?” When you can immediately identify that it’s different because these requests are coming from Android devices, with this particular build ID, using this language pack, in this region, with this app id, with a large payload … by now you probably know exactly what is wrong and why.

It’s not just about the unified data, either — although that is a huge part of it. It’s also about how effortlessly we handle high cardinality data, like unique IDs, shopping cart IDs, app IDs, first/last names, etc. The last generation of tooling cannot handle rich data like that, which is kind of unbelievable when you think about it, because rich, high cardinality data is the most valuable and identifying data of all.

How does improving observability translate into better business outcomes?

This is one of the other big shifts from the past generation to the new generation of observability tooling. In the past, systems, application, and business data were all siloed away from each other into different tools. This is absurd — every interesting question you want to ask about modern systems has elements of all three.

Observability isn’t just about bugs, or downtime, or outages. It’s about ensuring that we’re working on the right things, that our users are having a great experience, that we are achieving the business outcomes we’re aiming for. It’s about building value, not just operating. If you can’t see where you’re going, you’re not able to move very swiftly and you can’t course correct very fast. The more visibility you have into what your users are doing with your code, the better and stronger an engineer you can be.

Where do you see the future of observability heading, especially concerning AI developments?

Observability is increasingly about enabling teams to hook up tight, fast feedback loops, so they can develop swiftly, with confidence, in production, and waste less time and energy.

It’s about connecting the dots between business outcomes and technological methods.

And it’s about ensuring that we understand the software we’re putting out into the world. As software and systems get ever more complex, and especially as AI is increasingly in the mix, it’s more important than ever that we hold ourselves accountable to a human standard of understanding and manageability.

From an observability perspective, we are going to see increasing levels of sophistication in the data pipeline — using machine learning and sophisticated sampling techniques to balance value vs cost, to keep as much detail as possible about outlier events and important events and store summaries of the rest as cheaply as possible.

AI vendors are making lots of overheated claims about how they can understand your software better than you can, or how they can process the data and tell your humans what actions to take. From everything I have seen, this is an expensive pipe dream. False positives are incredibly costly. There is no substitute for understanding your systems and your data. AI can help your engineers with this! But it cannot replace your engineers.

Thank you for the great interview, readers who wish to learn more should visit Honeycomb.

[phonegap]

Explore the fundamentals of ETL pipelines, focusing on data extraction, transformation, and loading processes. Understand how these stages work together to streamline data integration and enhance organisational insights.

Know more at: https://bit.ly/3xOGX5u

[123albert]

Embark on a journey through the essential components of a data pipeline in our latest blog. Discover the building blocks that lay the foundation for an efficient and high-performance data flow.

[garymdm]

The Power of Automated Data Lineage: Validating Data Pipelines with Confidence

Introduction In today’s data-driven world, organizations rely on data pipelines to collect, process, and deliver data for crucial business decisions. However, ensuring the accuracy and reliability of these pipelines can be a daunting task. This is where automated data lineage comes into play, offering a solution to validate data pipelines with confidence. In this blog post, we will explore the…

[rajaniesh]

Maximize Efficiency with Volumes in Databricks Unity Catalog

With Databricks Unity Catalog's volumes feature, managing data has become a breeze. Regardless of the format or location, the organization can now effortlessly access and organize its data. This newfound simplicity and organization streamline data managem

[mytechnoinfo]

This blog showcase data pipeline automation and how it helps to boost your business to achieve its business goals.

[atozofsoftwareengineering]

Apache Spark in Machine Learning: Best Practices for Scalable Analytics

Hey friends! Check out this insightful blog on leveraging Apache Spark for machine learning. Discover best practices for scalable analytics with #ApacheSpark #MachineLearning #BigData #DataProcessing #MLlib #Scalability

Apache Spark is a powerful and popular open-source distributed computing framework that provides a unified analytics engine for big data processing. While Spark is widely used for various data processing tasks, it also offers several features and libraries that make it a valuable tool for machine learning (ML) applications. Apache Spark’s usage in Machine Learning Data Processing: Spark…

[knowledgehound]

Best Data Visualization Tools To Look Out For In 2023 - KnowledgeHound

According to a report by Fortune Business Insights, the global data visualization market stood at USD 8.85 billion in 2019 and is projected to reach USD 19.20 billion by 2027, exhibiting a CAGR of 10.2% during the forecast period.

The increasing use of the internet, smartphones, cloud computing technologies, advancements in machine learning, and the rising use of the Internet of Things are the driving factors behind the increase of the global data visualization market.

What is Data Visualization?

Before we discuss more on the data visualization tool, let’s first get a good understanding of data visualization. What does it mean?

According to KnowledgeHound Data visualization refers to the process of communication and translation of information and data in a visual context. As the name implies, data visualization often employs charts, graphs, bars, and other visual aids to describe the relationship between different types of data.

What are Data Visualization Tools?

Have you heard about Infogram or Google Charts? These are the tools that often support a variety of visual styles and are capable of handling a vast data volume.

Presenting data through visual elements makes it easy to analyze and understand. Here are the top 3 data visualization tools that are known for their incredible performance and impressive usability.

· Tableau

Tableau is one of the most commonly used data visualization tools, trusted by over 57,000 companies. Tableau provides integration for advanced databases, such as Amazon AWS, SAP, Hadoop, and My SQL, and creates graphics and visualizations from ever-evolving datasets often used for machine learning and artificial intelligence.

While tableau offers excellent visualization capabilities and supports connectivity with a wide range of data sources, the pricing is on the higher side. This can be seen as a con by many people.

· Google Charts

Google Charts has been one of the major players in the market space for a long time. Google Charts coded with HTML 5 and SVG have the capability to produce pictorial data and graphical data.

This data visualization tool provides users with unmatched cross-platform compatibility with android and iOS. Google Charts offer a user-friendly platform, is compatible with Google products, and has visually attractive data graphs. It is also easy to integrate the data.

However, it lacks customization abilities and network connectivity which is often required for visualization. Also, there are inadequate demos available on how to use the tool.

· Zoho Reports

Also called Zoho Analytics, Zoho Reports is a data visualization tool that integrates online reporting services with business intelligence. This allows quick creation and sharing of the reports in a few minutes.

This data visualization tool also supports the import of big data from databases and applications. Zoho offers the ability to create reports and modify them effortlessly. It also includes different features such as report sharing and email scheduling. The customer support is amazing.

However, the dashboard can seem confusing when you are dealing with a large volume of data.

While you can easily get your hands on some amazing data visualization tools, make sure to take your time to try out a few tools before settling for the best data visualization tool for your business.

Also Read — What Is Data Management And Is It Really Important?

[qwikskills]

Resources for Professionals looking to build their knowledge and skills in the field of data science

Data science is a rapidly growing field that requires professionals to have an understanding of various disciplines such as mathematics, statistics, programming, and machine learning. For professionals looking to build their knowledge and skills in the field of data science, there are a variety of resources available. These include online courses, tutorials, books, conferences, and bootcamps that can help individuals gain the necessary skills to become successful data scientists. Additionally, networking with experienced data scientists can provide valuable insight into the industry and help professionals stay up-to-date with the latest trends in data science. With the right resources and dedication to learning, anyone can become a successful data scientist.

Dataiku certification is a valuable resource for professionals who are looking to build their knowledge and skills in the field of data science. This certification program provides an opportunity for individuals to gain expertise in the Dataiku platform, which is used by many organizations around the world. The certification program covers topics such as data preparation, machine learning, natural language processing, and more. With this certification, individuals can demonstrate their proficiency in using Dataiku to create powerful data-driven solutions that drive business success.

[jcmarchi]

Datasets Matter: The Battle Between Open and Closed Generative AI is Not Only About Models Anymore

New Post has been published on https://thedigitalinsider.com/datasets-matter-the-battle-between-open-and-closed-generative-ai-is-not-only-about-models-anymore/

Datasets Matter: The Battle Between Open and Closed Generative AI is Not Only About Models Anymore

Two major open source datasets were released this week.

Created Using DALL-E

Next Week in The Sequence:

Edge 403: Our series about autonomous agents continues covering memory-based planning methods. The research behind the TravelPlanner benchmark for planning in LLMs and the impressive MemGPT framework for autonomous agents.

The Sequence Chat: A super cool interview with one of the engineers behind Azure OpenAI Service and Microsoft CoPilot.

Edge 404: We dive into Meta AI’s amazing research for predicting multiple tokens at the same time in LLMs.

You can subscribe to The Sequence below:

TheSequence is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

📝 Editorial: Datasets Matter: The Battle Between Open and Closed Generative AI is Not Only About Models Anymore

The battle between open and closed generative AI has been at the center of industry developments. From the very beginning, the focus has been on open vs. closed models, such as Mistral and Llama vs. GPT-4 and Claude. Less attention has been paid to other foundational aspects of the model lifecycle, such as the datasets used for training and fine-tuning. In fact, one of the limitations of the so-called open weight models is that they don’t disclose the training datasets and pipeline. What if we had high-quality open source datasets that rival those used to pretrain massive foundation models?

Open source datasets are one of the key aspects to unlocking innovation in generative AI. The costs required to build multi-trillion token datasets are completely prohibitive to most organizations. Leading AI labs, such as the Allen AI Institute, have been at the forefront of this idea, regularly open sourcing high-quality datasets such as the ones used for the Olmo model. Now it seems that they are getting some help.

This week, we saw two major efforts related to open source generative AI datasets. Hugging Face open-sourced FineWeb, a 44TB dataset of 15 trillion tokens derived from 96 CommonCrawl snapshots. Hugging Face also released FineWeb-Edu, a subset of FineWeb focused on educational value. But Hugging Face was not the only company actively releasing open source datasets. Complementing the FineWeb release, AI startup Zyphra released Zyda, a 1.3 trillion token dataset for language modeling. The construction of Zyda seems to have focused on a very meticulous filtering and deduplication process and shows remarkable performance compared to other datasets such as Dolma or RedefinedWeb.

High-quality open source datasets are paramount to enabling innovation in open generative models. Researchers using these datasets can now focus on pretraining pipelines and optimizations, while teams using those models for fine-tuning or inference can have a clearer way to explain outputs based on the composition of the dataset. The battle between open and closed generative AI is not just about models anymore.

🔎 ML Research

Extracting Concepts from GPT-4

OpenAI published a paper proposing an interpretability technique to understanding neural activity within LLMs. Specifically, the method uses k-sparse autoencoders to control sparsity which leads to more interpretable models —> Read more.

Transformer are SSMs

Researchers from Princeton University and Carnegie Mellon University published a paper outlining theoretical connections between transformers and SSMs. The paper also proposes a framework called state space duality and a new architecture called Mamba-2 which improves the performance over its predecessors by 2-8x —> Read more.

Believe or Not Believe LLMs

Google DeepMind published a paper proposing a technique to quantify uncertainty in LLM responses. The paper explores different sources of uncertainty such as lack of knowledge and randomness in order to quantify the reliability of an LLM output —> Read more.

CodecLM

Google Research published a paper introducing CodecLM, a framework for using synthetic data for LLM alignment in downstream tasks. CodecLM leverages LLMs like Gemini to encode seed intrstructions into the metadata and then decodes it into synthetic intstructions —> Read more.

TinyAgent

Researchers from UC Berkeley published a detailed blog post about TinyAgent, a function calling tuning method for small language models. TinyAgent aims to enable function calling LLMs that can run on mobile or IoT devices —> Read more.

Parrot

Researchers from Shanghai Jiao Tong University and Microsoft Research published a paper introducing Parrot, a framework for correlating multiple LLM requests. Parrot uses the concept of a Semantic Variable to annotate input/output variables in LLMs to enable the creation of a data pipeline with LLMs —> Read more.

🤖 Cool AI Tech Releases

FineWeb

HuggingFace open sourced FineWeb, a 15 trillion token dataset for LLM training —> Read more.

Stable Audion Open

Stability AI open source Stable Audio Open, its new generative audio model —> Read more.

Mistral Fine-Tune

Mistral open sourced mistral-finetune SDK and services for fine-tuning models programmatically —> Read more.

Zyda

Zyphra Technologies open sourced Zyda, a 1.3 trillion token dataset that powers the version of its Zamba models —> Read more.

🛠 Real World AI

Salesforce discusses their use of Amazon SageMaker in their Einstein platform —> Read more.

📡AI Radar

Cisco announced a $1B AI investment fund with some major positions in companies like Cohere, Mistral and Scale AI.

Cloudera acquired AI startup Verta.

Databricks acquired data management company Tabular.

Tektonic, raised $10 million to build generative agents for business operations —> Read more.

AI task management startup Hoop raised $5 million.

Galileo announced Luna, a family of evaluation foundation models.

Browserbase raised $6.5 million for its LLM browser-based automation platform.

AI artwork platform Exactly.ai raised $4.3 million.

Sirion acquired AI document management platform Eigen Technologies.

Asana added AI teammates to complement task management capabilities.

Eyebot raised $6 million for its AI-powered vision exams.

AI code base platform Greptile raised a $4 million seed round.

TheSequence is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

[sprinkledata]

Top 21 ETL Tools For 2023

In this digital world, everyday great volumes of data are being generated from varied sources, and companies want to give this data a form and structure by assembling it in an organized way and at a unified place to use it to their advantage. They want to analyze their data further to get a good understanding and to make well-informed data-driven decisions. To bring meaning to this raw data, ETL tools play a significant role and help businesses to take data analytics to the next level.

There are several ETL tools available in the market which automate this whole process of building data-pipeline, managing and monitoring them. In this article, we will understand the whole process of ETL in detail and ETL tools that are best suited to automate your data pipelines for accurate analysis.

What is ETL?‍

ETL stands for “Extract, Transform and Load”. ETL is a process of extracting data from different data sources, cleansing and organizing it, and eventually, loading it to a target data warehouse or a Unified data repository.

Why ETL?

In today's data-centric world ETL plays a vital role in maintaining the integrity of a company by keeping its data up to date. To get the correct insight it is therefore important to perform ETL mainly due to the following reasons: 

1. Data Volumes: The generated data has very high volume and velocity as many organizations have historical as well as real-time data being forged continuously from different sources.

2. Data Quality: The quality of the generated data is not exemplary as data is present in different formats like online feeds, online transactions, tables, images, excel, CSV, JSON, text files, etc. Data can be structured or unstructured, so to bring all different data formats to one homogeneous format performing the ETL process is highly needed.

To overcome these challenges many ETL tools are developed that make this process easy and efficient and help organizations combine their data by going through processes like de-duplicating, sorting, filtering, merging, reformatting, and transforming to make data ready for analysis.

Read more here: Best ETL tools for 2023

[valyrfia]

you say the only thing tethering me to this sport is a ship and i vehemently agree with you while trying to shove the python pipeline i built for fun to compare past race telemetry under the table

[arytha]

[ID from ALT: A fullbody digital drawing of my OC, Millenium, standing in front of a mirror that is reflecting her form after her death, Mimi. Millenium's hands are clutched in front of her, insecure, her back turned to the camera. Mimi is posing with confidence, smirking with her arms up and hands pressed against the mirror's surface. Millenium has her blonde hair down, wearing a simple sweater and pleated skirt. Mimi is wearing a flashy outfit, with a dress that fades into a transparent skirt, a seethrough bodysuit underneath, and unattached sleeves. Her hair is pink and pulled into high twintails, with her short bangs dyed black. She's wearing cat ear headphones, with a cat tail peeking out from her skirt. The room reflected in the mirror behind Mimi is glitchy, while the room outside the mirror is dark and dreary. End ID]