10 best data processing software for your tech stack

You’ve got customer data scattered everywhere—in your CRM, your analytics, your marketing platforms, and more. 

But while you know that this data holds the key to better customer experiences, you don’t have the time to pull it all together yourself.

The right data processing software does that for you: it collects messy, siloed information and transforms it into insights you can use to make better decisions. These tools also ensure your data remains safe and compliant.

Here are 10 of the best data processing platforms to help you make the most of your data.

Key insights

• Using secure data processing tools builds trust with your customers. You position your product as a leader when you confidently demonstrate that you handle user data with care.

• Data processing tools are most valuable when they integrate with other tools in your tech stack, like your web analytics or CRM. These integrations help you feed more data into your data warehouse so your team can extract richer insights and build a complete picture of your users and how they use your product. For example, with Data Connect, your Contentsquare data flows into that ecosystem as well, so you can build experiences your customers will love.

• Investing in tools that clean and transform your data leads to better analysis and helps your organization improve its data maturity, as you’ll spend less time wrangling your data and more time extracting meaningful insights.

10 best data processing software

There are 5 main types of data processing tools that we’ll explore today:

1. Data collection tools

2. Data cleaning tools

3. Data transformation tools

4. Data analysis tools

5. Data visualization tools

Each one helps you with a different goal and serves a distinct purpose. Let’s explore some of the best data processing technology.

Data collection tools

Data collection tools integrate data from multiple sources through APIs and databases. At a high level, data collection can fall into 2 categories:

1. Streaming: the tool captures data and makes it available for processing right away. Streaming capabilities are ideal when you need on-demand data. For example, for fraud detection or to give users real-time recommendations (like Netflix does). Streaming can be an expensive option as it’s always ‘on’.

2. Batch: the tool captures data over a period of time and processes it after collection. Think analysis after you wrap up a marketing campaign. Batch collection is typically more cost-effective than streaming.

And you don’t necessarily need to pick one or the other; teams can use both streaming and batch collection tools depending on their needs and available resources. 

Here are 2 data collection tools that offer different collection methods. 

1. Apache Kafka 

Apache Kafka is an open-source event streaming platform that continuously collects data from databases, sensors, mobile devices, and cloud applications. And because Apache Kafka is continuously collecting and organizing data, it works well for teams that need to collect and process high volumes of real-time data.

Data events are organized into topics (think of topics like files that store similar information, such as a topic for online purchases). This categorization makes it possible to organize large amounts of data so teams can scale their analysis and process streams efficiently. 

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2. Airbyte

Airbyte is an open-source data collection tool that gathers data from multiple sources. It’s primarily batch-oriented and allows users to select how often the tool collects data, making it ideal for working with large datasets without the costs that come with streaming. 

Airbyte also has a Change Data Capture (CDC) functionality, which allows the tool to capture and sync data as changes happen. For example, a marketing analytics team might use CDC to keep their reporting database updated with customer transactions. Instead of reloading entire tables multiple times a day, CDC captures only new purchases.

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🔥 Contentsquare’s Data Connect takes your user data and moves it straight into your data warehouse without any complex integrations. Keep all your data in one place so you can make smart decisions that move your team toward your goals. 

Data cleaning tools

Data cleaning tools remove duplicates, fix errors, standardize formats, and handle missing values. After cleaning, you’re left with data that’s easier to work with and analyze. 

Here are 2 tools we recommend.

3. OpenRefine

OpenRefine is a free, open-source tool that cleans and transforms data (we’ll discuss data transformation tools next). It runs locally on your own machine, making it ideal for smaller teams on a budget where cross-functional sharing isn’t necessary.

With OpenRefine, you can

• Cluster and edit data

• Reconcile data

• View the history of all data changes and revert back to certain steps

• Use facets to quickly look for patterns in your data 

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4. Alteryx 

Alteryx is a 3-in-1 tool that lets teams clean, transform, and analyze data directly in the platform. 

Its low- to no-code drag-and-drop UI lets non-technical users get value from the tool without intensive training, while advanced users can use Python, R, or SQL to create more complex workflows. 

Alteryx also lets teams automate repetitive tasks, run predictive analytics, and build interactive charts to help visualize data.

Data transformation tools 

Data transformation tools prepare your data for analysis by extracting it from databases, loading it into an analysis system, and transforming it into a usable format. 

For example, say you collect data on how much money users spend before they churn. If you have localized ecommerce stores, you might have data in different currencies like US dollars and euros. Transformation can convert these amounts into one currency for easier analysis. 

Let’s look at 2 well-known tools.

5. Apache Spark

Apache Spark is an open-source data transformation tool that can extract, transform, and load data from multiple sources, making it ideal for teams with massive datasets.

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You can run Spark on a single machine (this is good if you want to transform data locally), or on a cluster of multiple computers (this is ideal for transforming large amounts of data in parallel). 

Spark supports batch and stream processes, and in-memory computing speeds up complex operations, like joins and filtering across large datasets.

6. Talend

Talend is a low-code data transformation platform that helps teams extract, transform, and load data from multiple sources like databases, cloud storage, and APIs.

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Talend offers a visual drag-and-drop interface that helps users design data pipelines (automated workflows that transform and move data to your source destination) without extensive coding. 

It also supports batch and real-time integration, data quality monitoring, and automated workflows. 

Data analysis tools

Data analysis tools let you take your usable data and perform calculations and analysis on it so you can extract useful insights to improve your product, user experience, and other aspects of your business. 

Consider these 2 data analysis tools:

7. Excel

Excel is a widely used spreadsheet tool that needs no introduction. With Excel, teams can perform calculations, create pivot tables, and visualize data through charts and graphs.

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Excel’s built-in calculations help teams quickly summarize and analyze their data with basic calculations like simple sums, and more complex logical operations like calculating customer lifetime value (LTV).

Excel is usually sufficient for small to medium datasets. However, its current row limit is 1,048,576 (we counted 😉) and too much data—along with complex formulas, pivot tables, and charts—might slow down the program. 

8. Python

Python is a programming language capable of powerful data analysis thanks to its many libraries (bundles of pre-written code you can use to speed up your analysis).

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You can run Python locally or use shared cloud-based Python environments (like Google Colab or Databricks) to collaborate with your team.

Python is ideal for medium to large datasets or when you need advanced statistical analysis like predictive modeling. For example, a software company could use Python to analyze millions of user events across its platform to detect usage patterns to try and increase product adoption. 

💡Pro tip: need help making sense of your numbers? Contentsquare’s AI Agent, Sense, does the heavy lifting for you by analyzing your data and giving you clear insight.

• Use Session Replay Summaries to quickly reveal user behavior patterns without needing to sit through hours of recordings

• Summarize user feedback so you can get to the heart of what users want faster

• Ask the AI chatbot questions and get immediate insight for questions like “Which journey has the lowest conversion rate?” and “How do conversion rates compare from this week to last week?”

Get instant answers about your data with Sense, Contentsquare’s AI Agent

Data visualization tools

Data visualization tools create charts, dashboards, and other visualizations, enabling your team to understand their data and make informed decisions.

These 2 tools can help:

9. Tableau

Tableau is a data visualization platform that can quickly turn millions of data points into visuals that your entire team will understand. You can self-host Tableau, use it via the cloud, or download the desktop app to use locally. 

It has a drag-and-drop interface that makes it easy to create interactive dashboards, filter data, and drill down into important details for non-technical users.

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Advanced features, such as predictive modeling, make Tableau a valuable tool for those with more technical know-how. 

10. Looker Studio

Looker Studio is a free, cloud-based data visualization tool that lets teams build dashboards, reports, and charts. It’s simple to use and requires minimal setup, making it an ideal tool for marketing, product management teams, and small analytical teams. 

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Teams can use pre-built report templates to turn their data into charts within minutes, or design custom charts for custom analysis.

Looker Studio connects to a variety of data warehouses and data lakes so you can pull in data without opening support tickets or bugging your developers. 

Track your essential data with Contentsquare Dashboards 

Create custom dashboards that filter out the noise and highlight user data and metrics that mean the most to your team. Fill your dashboard with charts and graphs that are easy to interpret so you can make better decisions that will lead to happier customers.

Create as many custom dashboards as you need with Contentsquare Dashboards

How to choose a data processing tool

Choosing the right data processing tool can feel overwhelming with the wide variety of options available. But we’ve made it easier for you by coming up with a framework to guide your decision:

1. Assess your needs and goals: review how much data you have, what state it’s in (is it raw or cleaned?), and what goals you want to achieve with your data analytics. This helps you pinpoint which type of data analysis tools you need in your stack. 

2. Consider your team’s expertise: is your team comfortable with Python/SQL, or do they prefer no-code tools? How much time and money can you invest in training? Answering these questions can narrow down your choices for data processing software and find one your team will actually use.

3. Balance budget against needs: set a realistic price range for your tools based on the features you need and the resources you have. Include training and education within your budget if you will need to get your team up to speed with your chosen tool(s). 

Once you have a list of tools narrowed down, evaluate each one and make sure they

💪 Integrate data from multiple sources, for stronger analysis 

🧑‍💻 Are easy for your team to use, for higher product adoption

📈Scale with your growth, to future-proof your investment

🤝Integrate with your tech stack, to get the most out of all your tools

😊Comply with privacy laws and keep your data safe, to build user trust

Following this framework not only helps you pick a data processing software—it helps you pick one that scales with your business.

Build your data processing tech stack

The right combination of data processing tools transforms dispersed data into actionable insights.

And with enterprise-grade security backed by ISO 27001 and SOC 2 certifications, Contentsquare keeps your user data safe while integrating with the data processing tools of your choice.

By combining Contentsquare’s behavior insights with the rest of your data, you’ll have everything you need to build experiences and products your customers will love.

FAQs about data processing software