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Excel Json Parser: Json Explained

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JSON (JavaScript Object Notation) is a modern, lightweight and versatile data interchange format used to exchange data between web-based applications and services. With its ability to represent most standard data types and its simple syntax, many developers and administrators now prefer to work with JSON compared to other data formats like XML. At its core, JSON stores data objects as collections of key-value pairs, where the keys are always strings, and the values can consist of either strings, numbers, booleans, arrays or even other objects.

What is JSON?

JSON is a language-independent data format which is self-describing and easy for programmers to read/write quickly. It was first popularized with AJAX in 2005 and has become the de facto standard for structured data exchange within applications and services. As you might expect, each programming language has different ways of writing JSON, but its core syntax remains consistent across platforms. JSON data is composed of key-value pairs, where a key is a string, or a reference to an object, and the value can be anything from a string or number, up to complex data structures. In addition, any piece of JSON-encoded data must be wrapped in double quotes to avoid misinterpretation by systems that don’t adhere to the standard. For example, let’s say we’re storing contact information:

{ “firstName”: “John”, “lastName”: “Smith”, “address”: { “street”: “123 Main St.”, “city”: “New York”, “state”: “NY” }}

JSON is a powerful tool for data exchange, as it allows for the transfer of complex data structures between applications and services. It is also a great way to store data, as it is lightweight and can be easily parsed and manipulated. Additionally, JSON is a great way to share data between different programming languages, as its syntax is consistent across platforms. This makes it an ideal choice for data exchange between applications and services.

How to Parse JSON in Excel

While it may not be the most straightforward application to work with, Excel does offer JSON parsing capabilities through its Power Query tool. To utilize Power Query’s json parsing functionality, provide the tool with a JSON data set as input (either as a file or from an API response string). It will then provide you with an expansive list of data-specific tables which represent your objects in structured form. After selecting the desired columns from each table, Excel users can then use the conventional filtering techniques to extract the values they are interested in.

Once the desired values have been extracted, Excel users can then use the data in a variety of ways. For example, they can use the data to create charts and graphs, or to perform calculations and analysis. Additionally, the data can be used to create pivot tables, which can be used to quickly summarize and analyze large amounts of data. With the help of Power Query, Excel users can easily parse JSON data and use it to their advantage.

JSON Parsing Tips and Tricks

If you’re looking to get up to speed quickly with an Excel JSON parser, there are a few tips and tricks that can help you along the way. For starters, it can often be useful to use “Pretty Print” as it allows you to visually inspect the JSON data while Power Query still works out the values and column titles in the background. To do this, open Power Query, add an empty column and then paste your formatted JSON data into the new column.

Also, if the JSON string is fairly large (such as from an API response), Power Query may take longer than expected to parse it. This is due to the sheer amount of object references used by Power Query’s powerful query engine. If this is the case, consider using the “Edit Query Settings” option and setting the “Optimize for Performance” flag.

Benefits of Excel JSON Parser

Using Excel’s built-in JSON parser offers many benefits for users compared to alternative approaches. For one thing, users save time when they use Excel over alternative tools — they can easily move between different sheets and store large volumes of data with ease. Furthermore, the highly visual interface makes it easy to see the structure of the data and understand how components relate to one another.

In addition, Excel’s JSON Parser uses Power Query to read data directly from web sources like API calls and databases. This gives users access to real-time data and insights into complex datasets like customer feedback surveys or customer sales transactions.

Challenges of Parsing JSON in Excel

Despite these benefits, there are also certain challenges associated with using Excel for JSON parsing. One key issue that users may encounter is that Excel can interpret certain text elements differently than intended — for example, an apostrophe (‘) could be interpreted as an opening or closing quotation mark or other character. This can lead to unexpected results which users need to account for.

Also, since Microsoft’s JSON parser uses Power Query as its query engine, it is limited in scope when it comes to the types of records it can parse — for example, if you need to parse records from an unstructured dataset then external tools may be needed.

Best Practices for Using an Excel Json Parser

In order to maximize value from an Excel JSON parser, there are a few best practices that users should adhere to. For starters, users should ensure that all input data is carefully inspected prior to running any queries. This includes manually checking for any coding errors that may have occurred during transmission and taking steps to ensure that the code conforms to the JSON standard.

Furthermore, when extracting values from larger datasets, it helps significantly if users create layers of abstraction — that is, introduce index columns which map linked records together. This will improve query performance significantly and can help to avoid unexpected errors during parsing.

Troubleshooting Common Issues with JSON Parsing in Excel

The most common issues with Excel date parsing arise when the input data isn’t formatted properly. When this happens, there are several possible solutions or workarounds. The first solution is to validate user input — that is, have some sort of validation procedure in place which verifies that user input conforms to the standards set by your organization or industry.

Another solution is to create predefined data sets where fields are split into separate worksheets and formula-based relationships are established between them. This will ensure that redundant records don’t get created during parsing and unexpected errors are avoided.

Integrating an Excel Json Parser into Your Workflow

Integrating an Excel JSON parser into any existing workflow is fairly straightforward. Once the desired columns have been selected from your input file or API response (or are generated automatically through formula transformation), you can use either Power Pivot or Power Query to start extracting useful insights from your data.

If you’re using Power Pivot, then you can leverage its built-in relationships between different records via the use of hierarchical tables. This can be especially useful for non-relational databases like customer transaction records where you can create parent/child relationships between items for reporting/summarizing purposes.

Conclusion

Excel’s built-in JSON Parser makes it incredibly easy to extract values from data sets stored in this popular format. With its ability to easily parse complex datasets and relationships into structured tables, users save time by not having to perform manual coding or extract values through trial and error techniques.

However, despite these benefits, there are still certain challenges associated with using Excel for JSON parsing such as unexpected results due to incorrect formatting or slower query processing times due to large record sets. Users should take active steps to avoid these issues by setting up validations and predefined dataset structures.

Anand Das

Anand Das

Anand is Co-founder and CTO of Bito. He leads technical strategy and engineering, and is our biggest user! Formerly, Anand was CTO of Eyeota, a data company acquired by Dun & Bradstreet. He is co-founder of PubMatic, where he led the building of an ad exchange system that handles over 1 Trillion bids per day.

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