#code development

[geminisee]

x

[jcmarchi]

10 Best Data Integration Tools (September 2024)

New Post has been published on https://thedigitalinsider.com/10-best-data-integration-tools-september-2024/

10 Best Data Integration Tools (September 2024)

Data is the core component of effective organizational decision-making. Today, companies generate more data – over 145 zettabytes in 2024 – through sources like social media, Internet-of-Things (IoT) sources, and point-of-sale (POS) systems.

The challenge? Compiling data from these disparate systems into one unified location.

This is where data integration comes in!

Data integration is the process of combining information from multiple sources to create a consolidated dataset. While essential, it’s often time-consuming, resource-intensive, and prone to error when done manually.

Data integration tools can simplify this process, allowing businesses to streamline workflows and gain more actionable insights faster.

This blog will explore the benefits of data integration tools, the different types available, and the best tools to consider in 2024.

Benefits of Data Integration Tools

Data integration tools are designed to unify disparate data into a centralized system, making it easier for businesses to analyze and use for forecasting, marketing, and operational decisions..

This is important because 3 out of 4 organizations suffer from data silos, leading to inefficient decision-making due to incomplete information. Data integration tools consolidate this data, breaking down silos.

Data often comes in different formats depending on the source. These tools help standardize this data, ensuring consistency. Moreover, data integration tools can help companies save $520,000 annually by automating manual data pipeline creation.

Further, data integration tools create a unified, single source of truth for an organization’s data, leading to more accurate reporting, better data sharing, and improved decision-making.

Types of Data Integration Tools

Data integration tools come in various forms, with each catering to different organizational needs:

On-Premise Tools: These software are installed on and operated from a customer’s own in-house/local servers. They offer greater control over data security but can be resource-intensive.

Cloud-Based Tools: Hosted in the cloud or online, these tools offer more flexibility and scalability than on-premise tools.

Open-Source Tools: These tools are available free of charge and usually require coding expertise.

All-in-One Solutions: These are platforms offer functionalities beyond data integration. They can provide data transformation, validation, extraction, and governance capabilities alongside integration.

The Top 10 Data Integration Tools in 2024

The best data integration tools are not just technically robust – they also offer many non-tech benefits. Here are the top options to consider in 2024:

Fivetran is an established software in the data integration category. It offers fully automated data movement, enabling businesses to centralize their data in a warehouse. Fivetran also provides robust data security and governance. It can be deployed on-premise, in the cloud, or in a hybrid manner.

Key Features: Automated data pipelines with real-time updates, pre-built connectors, and hands-off maintenance.

Pros: Robust security certifications, automated updates, real-time data replication.

Cons: Limited customization options, opaque pricing, weak data transformation features.

Visit Fivetran →

Talend provides robust data integration, transformation, and data governance capabilities. It offers both open-source and enterprise/paid versions and facilitates big data management.

Key Features: Seamless integration with cloud and on-premise environments, extensive data quality, and governance tools.

Pros: Scalable, strong data governance features, support for big data.

Cons: It can be resource-intensive and requires technical skills for setup and management.

Visit Talend →

Informatica is an enterprise-level data management platform covering data integration, governance, and quality. It is known for its powerful data processing and transformation capabilities.

Key Features: Workflow automation, cloud-native architecture, and end-to-end data governance.

Pros: Robust functionality, highly scalable, large user community.

Cons: Costly for small to mid-sized businesses, steep learning curve.

Visit Informatica →

A product under the Pentaho brand by Hitachi Vantara, Pentaho Data Integration is a no-code data integration platform. It provides a drag-and-drop graphical UI for building data pipelines and is deployable on-premises and on the cloud.

Key Features: Extensive extract, transform, and load (ETL) functions, data integration, and data preparation – all in one platform.

Pros: Reusable transformation templates, large library of pre-built connectors, real-time data validation.

Cons: High learning curve for new users, limited customer support.

Visit Pentaho →

Jitterbit specializes in API integration, allowing businesses to connect to software-as-a-service (SaaS), on-premise, and cloud applications. It is ideal for organizations looking to build integrations quickly.

Key Features: Pre-built connectors, API integration, and automation tools for hybrid cloud and on-premise systems.

Pros: Fast API integrations, great for connecting disparate cloud-based systems.

Cons: Limited on-premise support and data mapping features.

Visit Jitterbit →

6. Hevo Data

Hevo Data is a real-time data integration platform designed for simplicity and speed. It provides connectivity to over 150+ sources and destinations.  Moreover, it is focused on replicating and streaming data in real-time and is trusted by over 2500+ companies.

Key Features: Real-time data replication and integration with major data warehouses.

Pros: Real-time updates, easy-to-use UI, seamless trial experience.

Cons: Confusing transformations, lack of pipeline categorization, view sync issues.

Visit Hevo Data →

7. Boomi

Boomi, formerly a Dell company, is an integration-as-a-service (IaaS) and API management platform. It also offers EDI management features alongside data governance. They are known for their expertise in Mergers & Acquisitions/Divestiture data movement.

Key Features: Cloud-native platform with powerful data migration capabilities.

Pros: Excellent reusability of components, extensive connectors, quick bug fixes.

Cons: Comparatively weak documentation, lack of robust auditability, low-code nature requires some coding knowledge.

Visit Boomi →

Airbyte is an open-source data movement platform with paid tiers. It’s designed for enterprises looking to leverage generative AI (GenAI). Airbyte has a 300+ library of connectors and the functionality to create custom ones. It also provides both low-code and no-code development.

Key Features: Customizable connectors, automated data syncing, open-source.

Pros: Available as a library in Python, one of the largest user communities, flexible sync frequency.

Cons: Troubleshooting failed syncs is difficult, API docs are unclear, and job execution features can be more developed.

Visit Airbyte →

SAP Data Services is part of the SAP Business Technology Platform. It is an enterprise-grade data integration solution offering tools for data quality and cleansing.

Key Features: Data integration, cleansing, transformation, and governance tools integrated with other SAP applications..

Pros: Apache integration, big data support, real-time data management.

Cons: Error descriptions are too vague, lack of cloud connectivity options, and debugging is difficult.

Visit SAP Data Services →

10. Astera

Astera is an AI-powered no-code data management platform that allows businesses to effortlessly perform end-to-end data management. It provides data extraction, integration, warehousing, API management, and electronic data management (EDI) management functionalities.

Key Features: No-code user interface (UI) with drag-and-drop architecture, custom pre-built connectors, and AI automation.

Pros: It is easy to use for non-technical users, supports multiple data sources, has high data mapping functionality, and offers highly rated customer service.

Cons: Documentation is complex, and optical character recognition (OCR) features are lacking.

Visit Astera →

Selecting the right tools for data integration can be difficult and time-consuming. Multiple factors are involved in the selection process. Let’s look at some essential criteria.

Factors to Consider When Selecting a Data Integration Tool

The ideal tool depends on the unique needs and stage of each business, which is characterized by the following factors:

Compatibility: Ensure the tool integrates seamlessly with your existing data sources and systems.

Ease of Use: No-code or low-code tools with drag-and-drop functionality are easy to use andgreat for  teams from all backgrounds.

Scalability: A good data integration tool should be scalable. On-premise solutions are limited in scalability, making them a sub-optimal choice for large and growing companies.

Cost: Evaluate the total cost of ownership, including licensing, maintenance, and support, as well as the expected return on investment (ROI).

Functionality: Assess the range of features, including automation, transformation, and governance.

Security: Look for robust security measures to protect sensitive data, especially in cloud-based environments with certifications like SOC 1 and 2, ISO 27001, etc.

Future Trends

AI is becoming common across industries – and data integration applications will also become more AI-focused, i.e., data mapping and ETL. As cloud services evolve, serverless data integration will also become a popular option.

Furthermore, with the growing use of cloud solutions, data integration-as-a-service (DIaaS) platforms will gain traction, offering businesses higher data scalability.

Explore Unite.ai for more resources on data integration and management.

[matthewcohnsblog]

Welcome.

Welcome to my Tumblr blog!

I'm interested in exploring my thought-cloud ☁️ about computing here.

I'm also interested in hearing your thoughts about distributed application development, content delivery networks, edge computing, and cloud computing.

I live in Vermont, not far from where I grew up.

[kizzer55555]

The Vampire Aesthetic

Ok so Danny knows two billionaires personally and they really couldn’t be more different. Yet they had one thing in common. A vampire aesthetic. Sam is fully into goth. Spiderwebs, bats, the color black. She enjoys fangs and fake blood and the darkness of her soul. Meanwhile, Vlad is Vlad. If his name wasn’t enough, the dark clothing, pale skin, and flying around with a cape and fangs with coffins in his mansion really sells it.

Danny doesn’t know many rich people so he thinks this might be some kind of trend. (If Paulina is rich, her family likes the chupacabra) So he just thinks that all rich people have some kind of vampire thing going on.

Cue Danny somehow ending in the Wayne household. Maybe he was brought over as a friend of one of the bats, maybe rescued from a field trip/vacation gone wrong, maybe some other situation. But he is there in civilian form with civilian Waynes and Danny just takes a good long look around the inside of the mansion.

“So where’s the vampire aesthetic?

Everyone freezes.

Danny just starts looking around, checking behind paintings and feeling the walls for secret levers. Used to secret passages with Vlad and possibly Sam. The Fentons definitely had them when they were temporarily rich.

“Come on, I know you guys are hiding it.”

Cue the entire batfamily thinking that this is another Tim and that he is fully aware that these people are the batfamily. Danny hangs around the mansion more and the bats just start dropping their disguises and not even bothering to hide stuff around Danny because they assume he already knows. (Possibly even trying to recruit him to be a new bat) Meanwhile, Danny, who does not know these people are batman and his birds, just does not pick up on any of it.

He grew up in a health violation with a giant ballon observatory lab above his head and a portal to the afterlife in his basement. He is a half dead teenager who has tea with the god of time and his godfather is the other parent to his clone child. He’s used to death lazers being scattered across his home and mysterious stains on clothing.

People are weird! He doesn’t judge!

[therian-database]

Hello there!

so, here's the deal:

I, a burnt out autistic highschooler, want to create an app for therians (as a therian myself).

Here's the basic features:

A database full of information on as many animals as possible (that can and will be updated!)

Blank templates for otherkins!

Each user has a profile- public or private- and can add theriotypes and notes about those theriotypes!

A journal feature where you can add pictures, drawings, and notes to a digital journal, similar to a glorified notes app lol- and you can make certain parts of your journal public or private!

Packs!

What are packs?

Have some word soup as an explanation: Basically if you want to create a pack to either make friends or add your friends, you can submit a pack request, where you explain why you want to make a pack and if you have specific people in mind to join, and I'm thinking that I don't want it to be like everyone has their own pack or whatever so that's why it has to be approved and stuff like I don't want too many different packs. Basically it's like a discord server or an amino thingy it's a big group chat and the pack creators can set up virtual events and send out pings like "hey! Join this zoom call if you wanna do some arts and crafts!!" Or smth like that

It's a database and a community!! If you would like to help out, please please please send an ask!! I would love all the support I can get!

P.S.

BE NICE AND DRINK WATER

[thatbadadvice]

Help! I'm a Perfect Genius, but This Potential Employer Asked Me a Boring Interview Question!

Ask A Manager, 13 Feb 2024:

I was rejected from a role for not answering an interview question. I had all the skills they asked for, and the recruiter and hiring manager loved me. I had a final round of interviews — a peer on the hiring team, a peer from another team that I would work closely with, the director of both teams (so my would-be grandboss, which I thought was weird), and then finally a technical test with the hiring manager I had already spoken to. (I don’t know if it matters but I’m male and everyone I interviewed with was female.) The interviews went great, except the grandboss. I asked why she was interviewing me since it was a technical position and she was clearly some kind of middle manager. She told me she had a technical background (although she had been in management 10 years so it’s not like her experience was even relevant), but that she was interviewing for things like communication, ability to prioritize, and soft skills. I still thought it was weird to interview with my boss’s boss. She asked pretty standard (and boring) questions, which I aced. But then she asked me to tell her about the biggest mistake I’ve made in my career and how I handled it. I told her I’m a professional and I don’t make mistakes, and she argued with me! She said everyone makes mistakes, but what matters is how you handle them and prevent the same mistake from happening in the future. I told her maybe she made mistakes as a developer but since I actually went to school for it, I didn’t have that problem. She seemed fine with it and we moved on with the interview. A couple days later, the recruiter emailed me to say they had decided to go with someone else. I asked for feedback on why I wasn’t chosen and she said there were other candidates who were stronger. I wrote back and asked if the grandboss had been the reason I didn’t get the job, and she just told me again that the hiring panel made the decision to hire someone else. I looked the grandboss up on LinkedIn after the rejection and she was a developer at two industry leaders and then an executive at a third. She was also connected to a number of well-known C-level people in our city and industry. I’m thinking of mailing her on LinkedIn to explain why her question was wrong and asking if she’ll consider me for future positions at her company but my wife says it’s a bad idea. What do you think about me mailing her to try to explain?

Sir,

You have been wronged in the most grievous of ways by a coven of retaliatory, self-aggrandizing women who have failed in the extreme to recognize your brilliance, your talent, and above all, your general superiority.

Of course you should mail this mediocre "grandboss" on LinkedIn to inform her of the deep offense she caused you by interviewing you in the first place, let alone doing so using a boring question — indeed, you have a moral and professional obligation to do so in order to preserve your honor and the honor of scores of men like you who have never done a single solitary thing wrong in their lives, ever.

But I beg you to consider doing more. A single, private message to one incompetent bitch may not convey to the necessary parties the depth and breadth of the situation. Many, many people have important lessons to learn from your experience, and I encourage you to share it widely. Consider making a public LinkedIn post, and ensure that it is shareable across platforms. Depending on your financial resources, a billboard with your name, professional headshot, and contact information could go a long way toward ensuring that everyone in your industry who needs to know just how you handled the way these women treated you, does know about it. I hope that in your continuing job search, you are able to connect with potential employers who have a much better grasp of all you bring to the table.

[magpieandpossum]

I'm already about 110% in love with these rough draft character sprites....

these are all unshaded, and will probably be continued to be edited, but I just wanted to share to get feedback! both garak and quark have alternate outfits as well ^^

[most-likely-fandom]

✌️✌️.

[jesncin]

(Don't Call Me Super-) Boy meets (Martian) Girl.

M'gann goes on a school exchange program in National City. Saul Erdel (scientist friend of the Martians) accompanies her while meeting with his other scientist friends in town. Luckily for M'gann, not everyone in National City's a stranger.

[codingquill]

Essentials You Need to Become a Web Developer

HTML, CSS, and JavaScript Mastery

Text Editor/Integrated Development Environment (IDE): Popular choices include Visual Studio Code, Sublime Text.

Version Control/Git: Platforms like GitHub, GitLab, and Bitbucket allow you to track changes, collaborate with others, and contribute to open-source projects.

Responsive Web Design Skills: Learn CSS frameworks like Bootstrap or Flexbox and master media queries

Understanding of Web Browsers: Familiarize yourself with browser developer tools for debugging and testing your code.

Front-End Frameworks: for example : React, Angular, or Vue.js are powerful tools for building dynamic and interactive web applications.

Back-End Development Skills: Understanding server-side programming languages (e.g., Node.js, Python, Ruby , php) and databases (e.g., MySQL, MongoDB)

Web Hosting and Deployment Knowledge: Platforms like Heroku, Vercel , Netlify, or AWS can help simplify this process.

Basic DevOps and CI/CD Understanding

Soft Skills and Problem-Solving: Effective communication, teamwork, and problem-solving skills

Confidence in Yourself: Confidence is a powerful asset. Believe in your abilities, and don't be afraid to take on challenging projects. The more you trust yourself, the more you'll be able to tackle complex coding tasks and overcome obstacles with determination.

[jcmarchi]

Top 10 Data Integration Tools in 2024

New Post has been published on https://thedigitalinsider.com/top-10-data-integration-tools-in-2024/

Top 10 Data Integration Tools in 2024

Data is the core component of effective organizational decision-making. Today, companies generate more data – over 145 zettabytes in 2024 – through sources like social media, Internet-of-Things (IoT) sources, and point-of-sale (POS) systems.

The challenge? Compiling data from these disparate systems into one unified location.

This is where data integration comes in!

Data integration is the process of combining information from multiple sources to create a consolidated dataset. While essential, it’s often time-consuming, resource-intensive, and prone to error when done manually.

Data integration tools can simplify this process, allowing businesses to streamline workflows and gain more actionable insights faster.

This blog will explore the benefits of data integration tools, the different types available, and the best tools to consider in 2024.

Benefits of Data Integration Tools

Data integration tools are designed to unify disparate data into a centralized system, making it easier for businesses to analyze and use for forecasting, marketing, and operational decisions..

This is important because 3 out of 4 organizations suffer from data silos, leading to inefficient decision-making due to incomplete information. Data integration tools consolidate this data, breaking down silos.

Data often comes in different formats depending on the source. These tools help standardize this data, ensuring consistency. Moreover, data integration tools can help companies save $520,000 annually by automating manual data pipeline creation.

Further, data integration tools create a unified, single source of truth for an organization’s data, leading to more accurate reporting, better data sharing, and improved decision-making.

Types of Data Integration Tools

Data integration tools come in various forms, with each catering to different organizational needs:

On-Premise Tools: These software are installed on and operated from a customer’s own in-house/local servers. They offer greater control over data security but can be resource-intensive.

Cloud-Based Tools: Hosted in the cloud or online, these tools offer more flexibility and scalability than on-premise tools.

Open-Source Tools: These tools are available free of charge and usually require coding expertise.

All-in-One Solutions: These are platforms offer functionalities beyond data integration. They can provide data transformation, validation, extraction, and governance capabilities alongside integration.

The Top 10 Data Integration Tools in 2024

The best data integration tools are not just technically robust – they also offer many non-tech benefits. Here are the top options to consider in 2024:

1. Fivetran

Fivetran is an established software in the data integration category. It offers fully automated data movement, enabling businesses to centralize their data in a warehouse. Fivetran also provides robust data security and governance. It can be deployed on-premise, in the cloud, or in a hybrid manner.

Key Features: Automated data pipelines with real-time updates, pre-built connectors, and hands-off maintenance.

Pros: Robust security certifications, automated updates, real-time data replication.

Cons: Limited customization options, opaque pricing, weak data transformation features.

2. Talend

Talend provides robust data integration, transformation, and data governance capabilities. It offers both open-source and enterprise/paid versions and facilitates big data management.

Key Features: Seamless integration with cloud and on-premise environments, extensive data quality, and governance tools.

Pros: Scalable, strong data governance features, support for big data.

Cons: It can be resource-intensive and requires technical skills for setup and management.

3. Informatica

Informatica is an enterprise-level data management platform covering data integration, governance, and quality. It is known for its powerful data processing and transformation capabilities.

Key Features: Workflow automation, cloud-native architecture, and end-to-end data governance.

Pros: Robust functionality, highly scalable, large user community.

Cons: Costly for small to mid-sized businesses, steep learning curve.

4. Pentaho Data Integration

A product under the Pentaho brand by Hitachi Vantara, Pentaho Data Integration is a no-code data integration platform. It provides a drag-and-drop graphical UI for building data pipelines and is deployable on-premises and on the cloud.

Key Features: Extensive extract, transform, and load (ETL) functions, data integration, and data preparation – all in one platform.

Pros: Reusable transformation templates, large library of pre-built connectors, real-time data validation.

Cons: High learning curve for new users, limited customer support.

5. Jitterbit

Jitterbit specializes in API integration, allowing businesses to connect to software-as-a-service (SaaS), on-premise, and cloud applications. It is ideal for organizations looking to build integrations quickly.

Key Features: Pre-built connectors, API integration, and automation tools for hybrid cloud and on-premise systems.

Pros: Fast API integrations, great for connecting disparate cloud-based systems.

Cons: Limited on-premise support and data mapping features.

6. Hevo Data

Hevo Data is a real-time data integration platform designed for simplicity and speed. It provides connectivity to over 150+ sources and destinations.  Moreover, it is focused on replicating and streaming data in real-time and is trusted by over 2500+ companies.

Key Features: Real-time data replication and integration with major data warehouses.

Pros: Real-time updates, easy-to-use UI, seamless trial experience.

Cons: Confusing transformations, lack of pipeline categorization, view sync issues.

7. Boomi

Boomi, formerly a Dell company, is an integration-as-a-service (IaaS) and API management platform. It also offers EDI management features alongside data governance. They are known for their expertise in Mergers & Acquisitions/Divestiture data movement.

Key Features: Cloud-native platform with powerful data migration capabilities.

Pros: Excellent reusability of components, extensive connectors, quick bug fixes.

Cons: Comparatively weak documentation, lack of robust auditability, low-code nature requires some coding knowledge.

8. Airbyte

Airbyte is an open-source data movement platform with paid tiers. It’s designed for enterprises looking to leverage generative AI (GenAI). Airbyte has a 300+ library of connectors and the functionality to create custom ones. It also provides both low-code and no-code development.

Key Features: Customizable connectors, automated data syncing, open-source.

Pros: Available as a library in Python, one of the largest user communities, flexible sync frequency.

Cons: Troubleshooting failed syncs is difficult, API docs are unclear, and job execution features can be more developed.

9. SAP Data Services

SAP Data Services is part of the SAP Business Technology Platform. It is an enterprise-grade data integration solution offering tools for data quality and cleansing.

Key Features: Data integration, cleansing, transformation, and governance tools integrated with other SAP applications..

Pros: Apache integration, big data support, real-time data management.

Cons: Error descriptions are too vague, lack of cloud connectivity options, and debugging is difficult.

10. Astera

Astera is an AI-powered no-code data management platform that allows businesses to effortlessly perform end-to-end data management. It provides data extraction, integration, warehousing, API management, and electronic data management (EDI) management functionalities.

Key Features: No-code user interface (UI) with drag-and-drop architecture, custom pre-built connectors, and AI automation.

Pros: It is easy to use for non-technical users, supports multiple data sources, has high data mapping functionality, and offers highly rated customer service.

Cons: Documentation is complex, and optical character recognition (OCR) features are lacking.

Selecting the right tools for data integration can be difficult and time-consuming. Multiple factors are involved in the selection process. Let’s look at some essential criteria.

Factors to Consider When Selecting a Data Integration Tool

The ideal tool depends on the unique needs and stage of each business, which is characterized by the following factors:

Compatibility: Ensure the tool integrates seamlessly with your existing data sources and systems.

Ease of Use: No-code or low-code tools with drag-and-drop functionality are easy to use andgreat for  teams from all backgrounds.

Scalability: A good data integration tool should be scalable. On-premise solutions are limited in scalability, making them a sub-optimal choice for large and growing companies.

Cost: Evaluate the total cost of ownership, including licensing, maintenance, and support, as well as the expected return on investment (ROI).

Functionality: Assess the range of features, including automation, transformation, and governance.

Security: Look for robust security measures to protect sensitive data, especially in cloud-based environments with certifications like SOC 1 and 2, ISO 27001, etc.

Future Trends

AI is becoming common across industries – and data integration applications will also become more AI-focused, i.e., data mapping and ETL. As cloud services evolve, serverless data integration will also become a popular option.

Furthermore, with the growing use of cloud solutions, data integration-as-a-service (DIaaS) platforms will gain traction, offering businesses higher data scalability.

Explore Unite.ai for more resources on data integration and management.

[rinisdrawing]

cozy summer afternoons

[ddeck]

they love working together

[renjunnipeikko]

dunno if this has been done yet but: airplane didn’t first become aware of peerless cucumber in the comments of pidw, he first saw peerless in the comments of a different web novel (that perhaps was one of airplane’s earlier works?) and so when initially drafting pidw he was inspired to write a villain just like cucumber bc “he’s MEAN MEAN for no reason, a literature snob, and pretends to be all high and mighty while looking down on others. also likes to kick down the little guy” and boom. sqq was born. cut to years later post-transmigration when sqh just casually drops this info with “lol i always did find it funny how much you hated the guy considering he’s literally based off you”

[skeletoninthemelonland]

a glimpse of what their early dynamic looks like