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6 Best Data Orchestration Tools to Transform Your Business

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6 Best Data Orchestration Tools to Transform Your Business

Data exists everywhere!

We use data every day — in different forms — to make informed decisions. It could be through counting your steps on a fitness app or tracking the estimated delivery date of your package. In fact, the data volume from internet activity alone is expected to reach an estimated 180 zettabytes by 2025.

Companies use data the same way but on a larger scale. They collect information about their targeted audiences through different sources, such as websites, CRM, and social media. This data is then analyzed and shared across various teams, systems, external partners, and vendors.

With the large volumes of data they handle, organizations need a reliable automation tool to process and analyze the data before use. Data orchestration tools are one of the most important in this process of software procurement.

What is Data Orchestration and Data Pipelines

Data orchestration is an automated process of data pipeline workflow. To break it down, let’s understand what goes on in a data pipeline.

Data moves from its raw state to a final form within the pipeline through a series of ETL workflows. ETL stands for Extract-Transform-Load. The ETL process collects data from multiple sources (extracts), cleans and packages the data (transforms), and saves the data to a database or warehouse (loads) where it is ready to be analyzed. Before this, data engineers had to create, schedule, and manually monitor the progress of data pipelines. But with data orchestration, each step in the workflow is automated.

Data orchestration is collecting and organizing siloed data from multiple data storage points and making it accessible and prepared for data analysis tools. With this automation act, businesses can streamline data from numerous sources to make calculated decisions.

The data orchestration pipeline is a game-changer in the data technology environment. The increase in cloud adoption from today’s data-driven company culturehas pushed the need for companies to embrace data orchestration globally.

Why is Data Orchestration Important

Data orchestration is the solution to the time-consuming management of data, giving organizations a way to keep their stacks connected while data flows smoothly.

“Data orchestration provides the answer to making your data more useful and available. But ultimately, it goes beyond simple data management. In the end, orchestration is about using data to drive actions, to create real business value.”

— Steven Hillion, Head of Data at Astronomer

As activities in an organization increase with the expansion of the customer base, it becomes challenging to cope with the high volume of data coming in. One example can be found in marketing. With the increased reliance on customer segmentation for successful campaigns, multiple sources of data can make it difficult to separate your prospects with speed and finesse.

Here’s how data orchestration can help:

  • Disparate data sources. Data orchestration automates the process of gathering and preparing data coming from multiple sources without introducing human error.
  • Breaks down silos. Many businesses have their data siloed, which can be a location, region, an organization, or a cloud application. Data orchestration breaks down these silos and makes the data accessible to the organization.
  • Removes data bottlenecks. Data orchestration eliminates the bottlenecks arising from the downtime of analyzing and preparing data due to the automation of this process.
  • Enforces data governance. The data orchestration tool connects all your data systems across geographical regions with different rules and regulations regarding data privacy. It ensures that the data collected complies with GDPR, CCPA, etc., laws on ethical data gathering.
  • Gives faster insights. Automating each workflow stage in the data pipeline using data orchestration gives data engineers and analysts more time to draw and perform actionable insights, to enable data-based decision-making.
  • Provides real-time information. Data can be extracted and processed the moment it is created, giving room for real-time data analysis or data storage.
  • Scalability. Automation of the workflow helps organizations scale data use through synchronization across data silos.
  • Monitoring the workflow progress. With data orchestration, the data pipeline is equipped with alerts to identify and amend issues as quickly as they occur.

Best Tools Data Orchestration Tools

Data orchestration tools clean, sort, arrange and publish your data into a data store. When choosing marketing automation tools for your business, two main things come to mind: what they can do and how much they cost.

Let’s look at some of the best ETL tools for your business.

1. Shipyard

Shipyard is a modern data orchestration platform that helps data engineers connect and automate tools and build reliable data operations. It creates powerful data workflows that extract, transform, and load data from a data warehouse to other tools to automate business processes.

The tool connects data stacks with up to 50+ low-code integrations. It orchestrates work between multiple external systems like Lambda, Cloud Functions, DBT Cloud, and Zapier. With a few simple inputs from these integrations, you can build data pipelines that connect to your data stack in minutes.

Some of Shipyard’s key features are:

  • Built-in notifications and error-handling
  • Automatic scheduling and on-demand triggers
  • Share-able, reusable blueprints
  • Isolated, scaling resources for each solution
  • Detailed historical logging
  • Streamlined UI for management
  • In-depth admin controls and permissions

Pricing:

Shipyard currently offers two plans:

  • Developer — Free
  • Team — Starting from $50 per month

2. Luigi

Developed by Spotify, Luigi builds data pipelines in Python and handles dependency resolution, visualization, workflow management, failures, and command line integration. If you need an all-python tool that takes care of workflow management in batch processing, then Luigi is perfect for you.

It’s open source and used by famous companies like Stripe, Giphy, and Foursquare. Giphy says they love Luigi for “being a powerful, simple-to-use Python-based task orchestration framework”.

Some of its key features are:

  • Python-based
  • Task-and-target semantics to define dependencies
  • Uses a single node for a directed graph and data-structure standard
  • Light-weight, therefore, requires less time for management
  • Allows users to define tasks, commands, and conditional paths
  • Data pipeline visualization

Pricing:

Luigi is an open-source tool, so it’s free.

3. Apache Airflow

If you’re looking to schedule automated workflows through the command line, look no further than Apache Airflow. It’s a free and open-source software tool that facilitates workflow development, scheduling, and monitoring.

Most users prefer Apache Airflow because of its open-source community and a large library of pre-built integrations to third-party data processing tools (Example: Apache Spark, Hadoop). The greater flexibility when building workflows is another reason why this is a customer favorite.

Some of its key features are:

  • Easy to use
  • Robust integrations with data cloud stacks like AWS, Microsoft Azure
  • Streamlines UI that monitors, schedules, and manages your workflows
  • Standard python features allow you to maintain total flexibility when building your workflows
  • Its latest version, Apache Airflow 2.0, has unique features like smart sensors, Full Rest API, Task Flow API, and some UI/UX improvements.

Pricing:

Free

4. Keboola

Keboola is a data orchestration tool built for enterprises and managed by a team of highly specialized engineers. It enables teams to focus on collaboration and get insights through automated workflows, collaborative workspaces, and secure experimentation.

The platform is user-friendly, so non-technical people can also easily build their data orchestration pipelines without the need for cloud engineering skills. It has a pay-as-you-go plan that scales with your needs and is integrated with the most commonly used tools.

Some of its key features are:

  • Runs transformations in Python, SQL, and R
  • No-code data pipeline automation
  • Offers various pre-built integrations
  • Data lineage and version control, so you don’t need to switch platforms as your data grows

Pricing:

Keboola currently has two plans:

5. Fivetran

Fivetran has an in-house orchestration system that powers the workflows required to extract and load data safely and efficiently. It enables data orchestration from a single platform with minimal configuration and code. Their easy-to-use platform keeps up with API changes and pulls fresh, rich data in minutes.

The tool is integrated with some of the best data source connectors, which analyze data immediately. Their pipelines automatically and continuously update, freeing you to focus on business insights instead of ETL.

Some of its key features are:

  • Integrated with DBT scheduling
  • Includes data lineage graphs to track how data moves and changes from connector to warehouse to BI tool
  • Supports event data flow data
  • Alerts and notifications for simplified troubleshooting

Pricing:

Fivetran has flexible price plans where you only pay for what you use:

  • Starter — $120 per month
  • Standard Select — $60 per month
  • Standard — $180 per month
  • Enterprise — $240 per month
  • Business Critical — Request a demo

6. Dagster

A second-generation data orchestration tool, Dagster can detect and improve data awareness by anticipating the actions triggered by each data type. It aims to enhance data engineers’ and analysts’ development, testing, and overall collaboration experience. It can also accelerate development, scale your workload with flexible infrastructure, and understand the state of jobs and data with integrated observability.

Despite being a new addition to the market, many companies like VMware, Mapbox, and Doordash trust Dagster for their business’s productivity. Mapbox’s data software engineer, Ben Pleasonton says, “With Dagster, we’ve brought a core process that used to take days or weeks of developer time down to 1-2 hours.”

Some of its key features are:

  • Greater fluidity and easy to integrate
  • Run monitoring
  • Easy-to-use APIs
  • DAG-based workflow
  • Various integration options with popular tools like DBT, Spark, Airflow, and Panda

Pricing:

Dagster is an open-source platform, so it’s free.

In conclusion…

Companies are increasingly relying on the best AI marketing tools for a sustainable, forward-thinking business. Leveraging automation has helped them accelerate their business operations, and data orchestration tools specifically have provided them with greater insights to run their business better.

Choosing the right ETL tools for your business largely depends on your existing data infrastructure. While our top picks are some of the best in the world, ensure you research well and select the best one to help your business get the most out of its data.

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MARKETING

SEO Recap: ChatGPT – Moz

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SEO Recap: ChatGPT - Moz

The author’s views are entirely his or her own (excluding the unlikely event of hypnosis) and may not always reflect the views of Moz.

We’re back with another SEO recap with Tom Capper! As you’ve probably noticed, ChatGPT has taken the search world by storm. But does GPT-3 mean the end of SEO as we know it, or are there ways to incorporate the AI model into our daily work?

Tom tries to tackle this question by demonstrating how he plans to use ChatGPT, along with other natural language processing systems, in his own work.

Be sure to check out the commentary on ChatGPT from our other Moz subject matter experts, Dr. Pete Meyers and Miriam Ellis:

Video Transcription

Hello, I’m Tom Capper from Moz, and today I want to talk about how I’m going to use ChatGPT and NLP, natural language processing apps in general in my day-to-day SEO tasks. This has been a big topic recently. I’ve seen a lot of people tweeting about this. Some people saying SEO is dead. This is the beginning of the end. As always, I think that’s maybe a bit too dramatic, but there are some big ways that this can be useful and that this will affect SEOs in their industry I think.

The first question I want to ask is, “Can we use this instead of Google? Are people going to start using NLP-powered assistants instead of search engines in a big way?”

So just being meta here, I asked ChatGPT to write a song about Google’s search results being ruined by an influx of AI content. This is obviously something that Google themselves is really concerned about, right? They talked about it with the helpful content update. Now I think the fact that we can be concerned about AI content ruining search results suggests there might be some problem with an AI-powered search engine, right?

No, AI powered is maybe the wrong term because, obviously, Google themselves are at some degree AI powered, but I mean pure, AI-written results. So for example, I stole this from a tweet and I’ve credited the account below, but if you ask it, “What is the fastest marine mammal,” the fastest marine mammal is the peregrine falcon. That is not a mammal.

Then it mentions the sailfish, which is not a mammal, and marlin, which is not a mammal. This is a particularly bad result. Whereas if I google this, great, that is an example of a fast mammal. We’re at least on the right track. Similarly, if I’m looking for a specific article on a specific web page, I’ve searched Atlantic article about the declining quality of search results, and even though clearly, if you look at the other information that it surfaces, clearly this has consumed some kind of selection of web pages, it’s refusing to acknowledge that here.

Whereas obviously, if I google that, very easy. I can find what I’m looking for straightaway. So yeah, maybe I’m not going to just replace Google with ChatGPT just yet. What about writing copy though? What about I’m fed up of having to manually write blog posts about content that I want to rank for or that I think my audience want to hear about?

So I’m just going to outsource it to a robot. Well, here’s an example. “Write a blog post about the future of NLP in SEO.” Now, at first glance, this looks okay. But actually, when you look a little bit closer, it’s a bluff. It’s vapid. It doesn’t really use any concrete examples.

It doesn’t really read the room. It doesn’t talk about sort of how our industry might be affected more broadly. It just uses some quick tactical examples. It’s not the worst article you could find. I’m sure if you pulled a teenager off the street who knew nothing about this and asked them to write about it, they would probably produce something worse than this.

But on the other hand, if you saw an article on the Moz blog or on another industry credible source, you’d expect something better than this. So yeah, I don’t think that we’re going to be using ChatGPT as our copywriter right away, but there may be some nuance, which I’ll get to in just a bit. What about writing descriptions though?

I thought this was pretty good. “Write a meta description for my Moz blog post about SEO predictions in 2023.” Now I could do a lot better with the query here. I could tell it what my post is going to be about for starters so that it could write a more specific description. But this is already quite good. It’s the right length for a meta description. It covers the bases.

It’s inviting people to click. It makes it sound exciting. This is pretty good. Now you’d obviously want a human to review these for the factual issues we talked about before. But I think a human plus the AI is going to be more effective here than just the human or at least more time efficient. So that’s a potential use case.

What about ideating copy? So I said that the pure ChatGPT written blog post wasn’t great. But one thing I could do is get it to give me a list of subtopics or subheadings that I might want to include in my own post. So here, although it is not the best blog post in the world, it has covered some topics that I might not have thought about.

So I might want to include those in my own post. So instead of asking it “write a blog post about the future of NLP in SEO,” I could say, “Write a bullet point list of ways NLP might affect SEO.” Then I could steal some of those, if I hadn’t thought of them myself, as potential topics that my own ideation had missed. Similarly you could use that as a copywriter’s brief or something like that, again in addition to human participation.

My favorite use case so far though is coding. So personally, I’m not a developer by trade, but often, like many SEOs, I have to interact with SQL, with JavaScript, with Excel, and these kinds of things. That often results in a lot of googling from first principles for someone less experienced in those areas.

Even experienced coders often find themselves falling back to Stack Overflow and this kind of thing. So here’s an example. “Write an SQL query that extracts all the rows from table2 where column A also exists as a row in table1.” So that’s quite complex. I’ve not really made an effort to make that query very easy to understand, but the result is actually pretty good.

It’s a working piece of SQL with an explanation below. This is much quicker than me figuring this out from first principles, and I can take that myself and work it into something good. So again, this is AI plus human rather than just AI or just human being the most effective. I could get a lot of value out of this, and I definitely will. I think in the future, rather than starting by going to Stack Overflow or googling something where I hope to see a Stack Overflow result, I think I would start just by asking here and then work from there.

That’s all. So that’s how I think I’m going to be using ChatGPT in my day-to-day SEO tasks. I’d love to hear what you’ve got planned. Let me know. Thanks.

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What Is a White Paper? [FAQs]

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What Is a White Paper? [FAQs]

The definition of a whitepaper varies heavily from industry to industry, which can be a little confusing for marketers looking to create one for their business.

The old-school definition comes from politics, where it means a legislative document explaining and supporting a particular political solution.

(more…)

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HubSpot to cut around 7% of workforce by end of Q1

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HubSpot to cut around 7% of workforce by end of Q1

This afternoon, HubSpot announced it would be making cuts in its workforce during Q1 2023. In a Securities and Exchange Commission filing it put the scale of the cuts at 7%. This would mean losing around 500 employees from its workforce of over 7,000.

The reasons cited were a downward trend in business and a “faster deceleration” than expected following positive growth during the pandemic.

Layoffs follow swift growth. Indeed, the layoffs need to be seen against the background of very rapid growth at the company. The size of the workforce at HubSpot grew over 40% between the end of 2020 and today.

In 2022 it announced a major expansion of its international presence with new operations in Spain and the Netherlands and a plan to expand its Canadian presence in 2023.

Why we care. The current cool down in the martech space, and in tech generally, does need to be seen in the context of startling leaps forward made under pandemic conditions. As the importance of digital marketing and the digital environment in general grew at an unprecedented rate, vendors saw opportunities for growth.

The world is re-adjusting. We may not be seeing a bubble burst, but we are seeing a bubble undergoing some slight but predictable deflation.


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About the author

Kim Davis

Kim Davis is the Editorial Director of MarTech. Born in London, but a New Yorker for over two decades, Kim started covering enterprise software ten years ago. His experience encompasses SaaS for the enterprise, digital- ad data-driven urban planning, and applications of SaaS, digital technology, and data in the marketing space.

He first wrote about marketing technology as editor of Haymarket’s The Hub, a dedicated marketing tech website, which subsequently became a channel on the established direct marketing brand DMN. Kim joined DMN proper in 2016, as a senior editor, becoming Executive Editor, then Editor-in-Chief a position he held until January 2020.

Prior to working in tech journalism, Kim was Associate Editor at a New York Times hyper-local news site, The Local: East Village, and has previously worked as an editor of an academic publication, and as a music journalist. He has written hundreds of New York restaurant reviews for a personal blog, and has been an occasional guest contributor to Eater.

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