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    {
      "text": "%md\n## Welcome to Zeppelin.\n##### This is a live tutorial, you can run the code yourself. (Shift-Enter to Run)",
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            "data": "\u003cdiv class\u003d\"markdown-body\"\u003e\n\u003ch2\u003eWelcome to Zeppelin.\u003c/h2\u003e\n\u003ch5\u003eThis is a live tutorial, you can run the code yourself. (Shift-Enter to Run)\u003c/h5\u003e\n\u003c/div\u003e"
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      "id": "20150213-231621_168813393",
      "dateCreated": "Feb 13, 2015 11:16:21 PM",
      "dateStarted": "Dec 17, 2016 3:32:15 PM",
      "dateFinished": "Dec 17, 2016 3:32:18 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Load data into table",
      "text": "import org.apache.commons.io.IOUtils\nimport java.net.URL\nimport java.nio.charset.Charset\n\n// Zeppelin creates and injects sc (SparkContext) and sqlContext (HiveContext or SqlContext)\n// So you don\u0027t need create them manually\n\n// load bank data\nval bankText \u003d sc.parallelize(\n    IOUtils.toString(\n        new URL(\"https://s3.amazonaws.com/apache-zeppelin/tutorial/bank/bank.csv\"),\n        Charset.forName(\"utf8\")).split(\"\\n\"))\n\ncase class Bank(age: Integer, job: String, marital: String, education: String, balance: Integer)\n\nval bank \u003d bankText.map(s \u003d\u003e s.split(\";\")).filter(s \u003d\u003e s(0) !\u003d \"\\\"age\\\"\").map(\n    s \u003d\u003e Bank(s(0).toInt, \n            s(1).replaceAll(\"\\\"\", \"\"),\n            s(2).replaceAll(\"\\\"\", \"\"),\n            s(3).replaceAll(\"\\\"\", \"\"),\n            s(5).replaceAll(\"\\\"\", \"\").toInt\n        )\n).toDF()\nbank.registerTempTable(\"bank\")",
      "user": "anonymous",
      "dateUpdated": "Dec 17, 2016 3:30:09 PM",
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        "msg": [
          {
            "type": "TEXT",
            "data": "import org.apache.commons.io.IOUtils\nimport java.net.URL\nimport java.nio.charset.Charset\nbankText: org.apache.spark.rdd.RDD[String] \u003d ParallelCollectionRDD[36] at parallelize at \u003cconsole\u003e:43\ndefined class Bank\nbank: org.apache.spark.sql.DataFrame \u003d [age: int, job: string ... 3 more fields]\nwarning: there were 1 deprecation warning(s); re-run with -deprecation for details\n"
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        ]
      },
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      "id": "20150210-015259_1403135953",
      "dateCreated": "Feb 10, 2015 1:52:59 AM",
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      "dateFinished": "Dec 17, 2016 3:30:58 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "text": "%sql \nselect age, count(1) value\nfrom bank \nwhere age \u003c 30 \ngroup by age \norder by age",
      "user": "anonymous",
      "dateUpdated": "Mar 17, 2017 12:18:02 PM",
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            "type": "TABLE",
            "data": "age\tvalue\n19\t4\n20\t3\n21\t7\n22\t9\n23\t20\n24\t24\n25\t44\n26\t77\n27\t94\n28\t103\n29\t97\n"
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        ]
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      "id": "20150210-015302_1492795503",
      "dateCreated": "Feb 10, 2015 1:53:02 AM",
      "dateStarted": "Dec 17, 2016 3:30:13 PM",
      "dateFinished": "Dec 17, 2016 3:31:04 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "text": "%sql \nselect age, count(1) value \nfrom bank \nwhere age \u003c ${maxAge\u003d30} \ngroup by age \norder by age",
      "user": "anonymous",
      "dateUpdated": "Mar 17, 2017 12:17:39 PM",
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          "maxAge": {
            "name": "maxAge",
            "defaultValue": "30",
            "hidden": false
          }
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      },
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        "msg": [
          {
            "type": "TABLE",
            "data": "age\tvalue\n19\t4\n20\t3\n21\t7\n22\t9\n23\t20\n24\t24\n25\t44\n26\t77\n27\t94\n28\t103\n29\t97\n30\t150\n31\t199\n32\t224\n33\t186\n34\t231\n"
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      },
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      "id": "20150212-145404_867439529",
      "dateCreated": "Feb 12, 2015 2:54:04 PM",
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      "dateFinished": "Dec 17, 2016 3:31:07 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "text": "%sql \nselect age, count(1) value \nfrom bank \nwhere marital\u003d\"${marital\u003dsingle,single|divorced|married}\" \ngroup by age \norder by age",
      "user": "anonymous",
      "dateUpdated": "Mar 17, 2017 12:18:18 PM",
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            "name": "marital",
            "defaultValue": "single",
            "options": [
              {
                "value": "single"
              },
              {
                "value": "divorced"
              },
              {
                "value": "married"
              }
            ],
            "hidden": false
          }
        }
      },
      "results": {
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            "type": "TABLE",
            "data": "age\tvalue\n19\t4\n20\t3\n21\t7\n22\t9\n23\t17\n24\t13\n25\t33\n26\t56\n27\t64\n28\t78\n29\t56\n30\t92\n31\t86\n32\t105\n33\t61\n34\t75\n35\t46\n36\t50\n37\t43\n38\t44\n39\t30\n40\t25\n41\t19\n42\t23\n43\t21\n44\t20\n45\t15\n46\t14\n47\t12\n48\t12\n49\t11\n50\t8\n51\t6\n52\t9\n53\t4\n55\t3\n56\t3\n57\t2\n58\t7\n59\t2\n60\t5\n66\t2\n69\t1\n"
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      "id": "20150213-230422_1600658137",
      "dateCreated": "Feb 13, 2015 11:04:22 PM",
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      "dateFinished": "Dec 17, 2016 3:31:09 PM",
      "status": "FINISHED",
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    },
    {
      "text": "%md\n## Congratulations, it\u0027s done.\n##### You can create your own notebook in \u0027Notebook\u0027 menu. Good luck!",
      "user": "anonymous",
      "dateUpdated": "Dec 17, 2016 3:30:24 PM",
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          {
            "type": "HTML",
            "data": "\u003cdiv class\u003d\"markdown-body\"\u003e\n\u003ch2\u003eCongratulations, it\u0026rsquo;s done.\u003c/h2\u003e\n\u003ch5\u003eYou can create your own notebook in \u0026lsquo;Notebook\u0026rsquo; menu. Good luck!\u003c/h5\u003e\n\u003c/div\u003e"
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      },
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    {
      "text": "%md\n\nAbout bank data\n\n```\nCitation Request:\n  This dataset is public available for research. The details are described in [Moro et al., 2011]. \n  Please include this citation if you plan to use this database:\n\n  [Moro et al., 2011] S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. \n  In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM\u00272011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.\n\n  Available at: [pdf] http://hdl.handle.net/1822/14838\n                [bib] http://www3.dsi.uminho.pt/pcortez/bib/2011-esm-1.txt\n```",
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      "dateUpdated": "Dec 17, 2016 3:30:34 PM",
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            "data": "\u003cdiv class\u003d\"markdown-body\"\u003e\n\u003cp\u003eAbout bank data\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCitation Request:\n  This dataset is public available for research. The details are described in [Moro et al., 2011]. \n  Please include this citation if you plan to use this database:\n\n  [Moro et al., 2011] S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. \n  In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM\u0026#39;2011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.\n\n  Available at: [pdf] http://hdl.handle.net/1822/14838\n                [bib] http://www3.dsi.uminho.pt/pcortez/bib/2011-esm-1.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/div\u003e"
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