Replicating SaaS Data in SharePoint with 3 Clicks

Super proud to announce Flatly supports data sync to SharePoint.

• 60+ SaaS Integrations to SharePoint
• 10K financial institutions to SharePoint
• Custom databases like Firebase and MySQL to SharePoint

SharePoint houses multiple flat file formats (yes XLSX of course) including interesting ones like NDJSON and Markdown.

This integration uses the AppFolder architecture. Which means better security and privacy. Cannot wait to see it in action, powering mission critical collaboration-oriented data pipelines.

Markdown is the new Warehouse

The move toward Markdown in production pipelines has accelerated only recently due to the rise of Retrieval-Augmented Generation (RAG) and Agentic Workflows.

Token Efficiency and Cost

Modern AI APIs charge per "token." JSON is notoriously token-expensive because the model must process every bracket and quote. Recent benchmarks show that Markdown can use 15% to 38% fewer tokens than JSON for the same data content, directly reducing the cost of running AI agents at scale (Source: Improving Agents).

Context Retention during "Chunking"

In RAG systems, large documents are broken into "chunks" to fit into a model's memory. If you chunk a JSON file, you risk breaking the syntax, leaving the AI with an unparseable fragment. Markdown, being plain text, is "chunk-safe." Even a fragment of a Markdown document usually contains enough structural clues (like a nearby header) for the AI to maintain context (Source: Anup Jadhav).

Accuracy Benchmarks

Recent stress tests (2025-2026) have shown that LLMs perform significantly better on "Key-Value Markdown" than on standard CSV. In some employee record retrieval tests, Markdown-formatted data resulted in 16% higher accuracy than CSV. The model's attention mechanism uses Markdown headers as "positional fences," helping it locate specific data points more reliably.

The Modern ETL Strategy

Contemporary ETL pipelines are now being built to "Target Markdown" as their final destination. Instead of loading raw data into a warehouse and then querying it, teams are extracting data, cleaning it, and rendering it into a Markdown Knowledge Base. This allows the same data to be audited by a human manager, version-controlled in Git, and ingested by a corporate AI assistant, all without further transformation.

Flatly supports data sync from numerous business data APIs to the markdown format in 10+ clouds.

Effortless Ways to Export Your Bank Transactions and Balances: A Guide

Managing your finances effectively involves more than just keeping a watchful eye on your spending. With the rise of digital banking, there now lies an opportunity to delve deeper into your financial habits that can help inform better budgeting decisions. Exporting your bank transactions and balances is the first step towards a comprehensive analysis of your personal banking data. But how exactly can you do this, and what should you be looking out for once you have your data at hand?

Let's start by exploring the methods to export your banking data, which can easily be done through online banking portals or mobile apps. Most banks offer an option to download your transactions in various formats such as CSV (Comma Separated Values), Excel, or even PDF. These formats allow for easy manipulation and review of your financial history.

"Your bank is not just a place to store your money—it’s a data warehouse that, if used effectively, can offer insights into your spending patterns and savings potential."

Once you have your data exported, the next important step involves setting up a system for analyzing it. Here are a few tried-and-tested practices:

• Categorize Expenses: Create categories for different types of spending, like groceries, utilities, and entertainment. This helps in identifying areas where you might be overspending.
• Identify Trends: Look for patterns in your spending habits over the months. Are there particular times when you spend more? Understanding these patterns can help you budget better.
• Set Budgetary Goals: With a clear view of your spending patterns, you can set realistic saving goals. Use past data to project future expenses and set achievable targets.
• Utilize Financial Tools: Consider using financial software or apps that can automatically categorize and analyze your transactions, providing you with insights and reports.

As you navigate this data, it's essential to keep learning and adapting. Regularly reviewing your financial data not only helps in making informed decisions but also creates a habit of financial discipline. The journey to financial literacy is built on small consistent actions—and there's no better time to start than now.

The beauty of analyzing personal banking data is the empowerment it brings. By understanding spending habits and cash flows, you can tailor your budget to better suit your needs, potentially paving the way for more personalized financial products. Start with exporting your bank transactions and balances; this can usually be done easily through your online banking platform.

Begin utilizing basic tools like spreadsheets to manage this data. These not only provide visual clarity but also minimize human errors, offering a reliable snapshot of your financial health. Spreadsheets serve as an excellent starting point before moving to more advanced solutions.

Furthermore, regularly check for accuracy. Make this part of your routine to ensure that your financial insights are grounded in reality. Accurate data means more effective budgeting, smarter financial decisions, and early detection of potential issues.

Remember, your financial information is sensitive, so handling it with care and maintaining compliance is crucial. Explore additional resources like FDIC.gov and ConsumerFinance.gov for more guidance on safeguarding your data and enhancing your financial literacy.

To efficiently manage and analyze your personal banking data, consider these practical steps:

• Exporting Data: Most banks offer online banking platforms where you can download your transaction history and account balances as spreadsheets or PDFs. Look for an 'Export' or 'Download' option in your online banking interface. This data can typically be exported to your preferred budgeting software or analyzed manually.
• Utilizing Financial Tools: Leverage budgeting tools and personal finance apps that integrate with your bank accounts. These tools can automatically track your spending, categorize expenses, and help you set and monitor financial goals. Popular apps include YNAB (You Need A Budget) and adjacent tools which are designed to simplify financial data analysis.

Replicating Wave Accounting Data to Cloud Storage

Wave Accounting has long been a trusted platform for entrepreneurs, freelancers, and small businesses looking for an intuitive way to manage finances. Its clean design and approachable tools make it an attractive choice.

But as with many business platforms, users often want more flexibility with their data: not just viewing it within Wave, but analyzing it elsewhere, combining it with other sources, or feeding it into dashboards. Traditionally, this has meant exporting CSVs or exploring Wave’s GraphQL API, which can require schema familiarity and manual effort.

Flatly’s new Wave Accounting integration removes that friction. In under three minutes, you can connect your Wave account and begin syncing clean, usable data directly into the cloud storage and business intelligence apps you already rely on.

No need to parse GraphQL schemas, build custom scripts, or manage exports. Flatly’s approach is point-and-go: simply choose the dataset—like Invoices, Customers, or Transactions—and Flatly handles the rest, delivering flat, structured data where you need it.

The integration supports eleven core datasets, from Accounts to Vendors, and allows filtering by date ranges when needed. For a full list of data sets supported in the Wave integration, see Destinations.

This gives businesses both breadth and precision: you can pull everything, or just what matters for a specific reporting cycle.

For destinations, Flatly supports a wide range of options, but four stand out as especially impactful for Wave users:

• Google Sheets – Perfect for quick collaboration, light analysis, and connecting with Google Data Studio.
• Excel Online – A natural fit for businesses already in the Microsoft ecosystem, where Wave data flows directly into familiar spreadsheets.
• Google BigQuery – A powerful option for advanced users who want to query Wave data at scale or combine it with other datasets in a warehouse environment.
• Amazon S3 – An excellent choice for archiving or integrating Wave data into broader ETL pipelines and data lakes.

By automating the flow of Wave data into these destinations, Flatly empowers businesses to focus on insights rather than integration.

The benefit is clear: your accounting data becomes instantly portable, analysis-ready, and aligned with the tools you already use. Flatly’s Wave Accounting integration turns what was once a manual, technical task into a simple, reliable workflow—making it easier than ever to put your financial data to work.