I tried the new blocks of Slack Block Kit [Data Edition]

Since 2026, new blocks have been added one after another to Slack's Block Kit. In this series, we will introduce these new blocks while actually trying them out.

• Part 1: Data Edition (this article)
• Part 2: Display Edition
• Part 3: Agent Edition
• Part 4: Streaming Edition
• Part 5: Validation & CI Edition

For this first installment, we will use the data table block and data visualization block to convert aggregated results into tables and charts and post them to a channel.

For the basics of Block Kit, please refer to the following article.

https://dev.classmethod.jp/articles/slack-building-block-kit/

Overview of the Two Blocks

The data table block is a table block added on May 20, 2026, that supports pagination, sorting, and filtering^[1].

Until now, to display a table in Slack, you had to either manually align columns using mrkdwn code blocks or use the table block[^2]. However, neither of these supported interactive operations. With the data table block, on the other hand, you can sort columns and paginate without complex configuration.

The data visualization block is a chart block added on June 16, 2026, that supports four types of charts: pie / bar / area / line^[2].

The established approach for charts has been to have the app generate images using matplotlib or similar tools and upload them via the files.upload API. With the data visualization block, however, you simply pass JSON to the blocks parameter of chat.postMessage, and rendering is handled by the Slack client. This means no image generation code and no upload round-trips are needed. How convenient!

data table block Specifications^[3]

Sorting is alphabetical by default, but numeric sorting is used only when all cells in a column are raw_number. The key point is to send numeric columns in aggregated results as raw_number rather than as strings.

data visualization block Specifications^[4]

As runtime validation rules, all data point label values must match one of the axis_config.categories values, series cannot omit data points corresponding to categories, and series names must not be duplicated.

Trying It Out

We will use assumed data aggregating the number of inquiries over the past 5 weeks, broken down by Web and app sources. This time, we will not create an app, but instead validate by pasting JSON into the Block Kit Builder.

For details on Block Kit Builder, please refer to the following article.

https://dev.classmethod.jp/articles/tips-slack-bot-block-kit-builder/

data table block

{
  "blocks": [
    {
      "type": "data_table",
      "caption": "Weekly Inquiry Count",
      "page_size": 3,
      "rows": [
        [
          { "type": "raw_text", "text": "Week" },
          { "type": "raw_text", "text": "Web" },
          { "type": "raw_text", "text": "App" }
        ],
        [
          { "type": "raw_text", "text": "Week of 6/29" },
          { "type": "raw_number", "value": 42, "text": "42" },
          { "type": "raw_number", "value": 31, "text": "31" }
        ],
        [
          { "type": "raw_text", "text": "Week of 7/6" },
          { "type": "raw_number", "value": 55, "text": "55" },
          { "type": "raw_number", "value": 28, "text": "28" }
        ],
        [
          { "type": "raw_text", "text": "Week of 7/13" },
          { "type": "raw_number", "value": 38, "text": "38" },
          { "type": "raw_number", "value": 45, "text": "45" }
        ],
        [
          { "type": "raw_text", "text": "Week of 7/20" },
          { "type": "raw_number", "value": 61, "text": "61" },
          { "type": "raw_number", "value": 39, "text": "39" }
        ],
        [
          { "type": "raw_text", "text": "Week of 7/27" },
          { "type": "raw_number", "value": 47, "text": "47" },
          { "type": "raw_number", "value": 52, "text": "52" }
        ]
      ]
    }
  ]
}

Clicking a column header sorts the table. Also, since page_size is set to 3, the 5 rows of data are split across 2 pages and a pagination UI is displayed. When omitted, it defaults to 5 rows per page.

data visualization block

We will turn the same data into a line chart.

{
  "blocks": [
    {
      "type": "data_visualization",
      "title": "Inquiry Count Trends",
      "chart": {
        "type": "line",
        "series": [
          {
            "name": "Web",
            "data": [
              { "label": "Week of 6/29", "value": 42 },
              { "label": "Week of 7/6", "value": 55 },
              { "label": "Week of 7/13", "value": 38 },
              { "label": "Week of 7/20", "value": 61 },
              { "label": "Week of 7/27", "value": 47 }
            ]
          },
          {
            "name": "App",
            "data": [
              { "label": "Week of 6/29", "value": 31 },
              { "label": "Week of 7/6", "value": 28 },
              { "label": "Week of 7/13", "value": 45 },
              { "label": "Week of 7/20", "value": 39 },
              { "label": "Week of 7/27", "value": 52 }
            ]
          }
        ],
        "axis_config": {
          "categories": ["Week of 6/29", "Week of 7/6", "Week of 7/13", "Week of 7/20", "Week of 7/27"],
          "x_label": "Week",
          "y_label": "Count"
        }
      }
    }
  ]
}

Hovering over the chart displays the count for that week in a tooltip.

Differences from the Existing table block

Here we organize the schema-level differences between the table block mentioned in the overview and the data table block.

Since the data table block does not have column_settings, the table block should be used when column formatting such as right-alignment is needed.

Summary

By leveraging the data table block and data visualization block, you can easily display sortable tables and charts in Slack simply by assembling the aggregated results into JSON.

If you have any periodic reporting apps that make use of tables or charts, why not consider migrating to these new blocks?

In the next Part 2, we will try out general-purpose display blocks. Stay tuned!