athena create table from json

Using this service can serve a variety of purposes, but the primary use of Athena is to query data directly from Amazon S3 (Simple Storage Service), without the need for a database engine. ‘paths’=’features,type’) You need to set the region to whichever region you used when creating the table (us-west-2, for example). The result looks similar to this: You can also use a Unix-like shell on your local computer or on an Amazon EC2 instance to populate a S3 location with the API data: Now we have the data in S3. 1 For Athena to read JSON, the data should be in a single line. I am using AWS Athena. ROW FORMAT SERDE ‘org.openx.data.jsonserde.JsonSerDe’ On the top level is an attribute called symbol, which identifies the stock described here: Apple. ‘s3://vicinitycheck/rawData/jsondata/’ The result looks similar to what you see below. [/sourcecode], 3. [/sourcecode], [sourcecode language=”plain”] In my case, the location of the data is s3://athena-json/financials, but you should create your own bucket. Create Table : [sourcecode language=”plain”] CREATE EXTERNAL TABLE jsondata (type string, features array>) ROW FORMAT SERDE ‘org.openx.data.jsonserde.JsonSerDe’ LOCATION ‘s3:////’ [/sourcecode] Query Table: [sourcecode language=”plain”] SELECT type AS TypeEvent, In this case, I needed to create 2 tables that holds you tube data from Google Storage. Amazon QuickSight picks up the data types that we defined in Athena. To do that, you have to create a schema declaration in AWS Glue, which basically says which “columns” exist and what their data types are. Currently we only support CSV and JSON storage formats. You can run this statement using the Athena console as depicted following: After you run the SQL statement on the left, the just-created table financials_raw is listed under the heading Tables. LOCATION ‘s3:////’ The example below introduced extra new lines for better readability only. type string, Applicable to experimental, rapidly evolving interpretations of data structures and use cases. As you can see from the screenshot, you have multiple options to create a table. For example, the original JSON file was 73 bytes. In this blog post, I show you how to use JSON-formatted data and translate a nested data structure into a tabular view. This can be extremely powerful, if such a dynamic and differentiated interpretation of the data is valuable. To create a table using the Athena add table wizard. We put our metric researchanddevelopment towards the value well, so that it’s displayed on the y-axis. To implement our example, we now have more than enough skills and we can leave it at that. “type”: “FeatureCollection”, All these options don’t replace what you learned in this article, but benefit from your being able to compare and contrast JSON formatted data and nested data. The first column shows the expression that can be used in a SQL statement like SELECT FROM financials_raw_json, where  is to be replaced by the expression in the first column. Working with tables. For this post, we’ll stick with the basics and select the “Create table from S3 bucket data” option.So, now that you have the file in S3, open up Amazon Athena. Remember the Athena table name which will be used later. In this blog post, we use it to provide data for visualization using Amazon QuickSight. This is a powerful concept and enables an iterative approach to data modeling. “first”: “raj”, In the following SQL statement, UNNEST takes the children column from the original table as a parameter. WHERE type = ‘FeatureCollection’ On the other hand, it takes more discipline to make sure that during maintenance different interpretations are not introduced by accident. Let’s also explore the alternative path that we discussed before. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. During our excursions, we never touched the actual data. If you want to use these concepts at scale, consider how to apply partitioning of data and possibly how to consolidate data into larger files. SELECT type AS TypeEvent, CREATE EXTERNAL TABLE jsondata ( “features”: [“latitude”, “longitude”] In the example following, financial data for only one year is shown. We used the view as an interface to Amazon QuickSight. We map the symbol and the list of financials as an array and some figures. ‘org.apache.hadoop.mapred.TextInputFormat’ You can use this slider to adjust the time frame shown. Then we cross-join each child with its parent, which creates an individual row for each child that contains the child and its parent. Your changes are immediately reflected in the visualization. Zappysys can read CSV, TSV or JSON files using S3 CSV File Source or S3 JSON File Source connectors. Once you execute query it generates CSV file. In the next dialog box, you can choose if you want to import the data into SPICE for quicker analytics or to directly query the data. CREATE EXTERNAL TABLE jsondata ( Step 3: Create Athena Table Structure for nested json along with the location of data stored in S3. Depending on the data, also consider whether storing it in a columnar fashion, using for example Apache Parquet might be beneficial. We then can run an Athena … Currently, Athena catalog manager doesn’t share Hive catalog; The following code snippets are used to create multiple versions of the same data set for experimenting with Athena. CSV, JSON, Avro, ORC, Parquet …) they can be GZip, Snappy Compressed. Athena creates a SELECT statement to show 10 rows of the table: Looking at the output, you can see that Athena was able to understand the underlying data in the JSON files. } Our view now is a data source for Amazon QuickSight and we can turn to visualizing the data. You can learn something new everyday, and today I learned that AWS Athena supports INSERT INTO queries. ‘classification’=’json’), [sourcecode language=”plain”] FROM blogpost.jsondata Don't forget to replace S3_BUCKET with the actual bucket containing the files. “geometry”: { Given that Amazon QuickSight picked up on the reportdate being a DATE, it provides a date slider at the bottom of the visual. Drag the handle at the lower-right corner to adjust the size to your liking. Follow the instructions from the first Post and create a table in Athena. “type”: “FeatureCollection”, SELECT type AS TypeEvent, features AS FeatherType The JSON contents can later be interpreted and the structures at query creation time mapped to columns. 1. The query above will create the table; the name of the fields are the same as the one from the JSON stored on S3. Creating Table in Athena from json file :FAILED: ParseException line 6:10 missing : at 'struct' near '' Amazon Athena is a serverless querying service, offered as one of the many services available through the Amazon Web Services console. The most workflow I've found for exporting data from Athena or Presto into Python is: Writing SQL to filter and transform the data into what you want to load into Python; Wrapping the SQL into a Create Table As Statement (CTAS) to export the data to S3 as Avro, Parquet or JSON lines files. Just like creating any other table field using the appropriate data type named method, we have created a JSON column using the json method with the name attributes. Thanks in advance Edited by: samara on May 9, 2018 7:16 AM In this post, we introduced CREATE TABLE AS SELECT (CTAS) in Amazon Athena. Today, we are releasing support for creating tables using the results of a Select query or support for Create Table As Select (CTAS) statement. The following table shows how to extract the data, starting at the root of the record in the first example. To flatten the data, we first unnest the individual children for each parent. In both approaches, the underlying data is not touched. We only defined different ways to interpret the data. The query above will create the table; the name of the fields are the same as the one from the JSON stored on S3. Such data can also help to add more finely grained facets to your understanding of customers and interactions. STORED AS INPUTFORMAT However, Athena is able to query a variety of file formats, including, but not limited to CSV, Parquet, JSON, etc. Furthermore, JSON data can be hierarchical, which must be unnested and cross-joined to provide the data in a flattened, tabular fashion. In this case, we defer the final decisions about the data structures from table design to query design. Create table and access the file. © 2020, Amazon Web Services, Inc. or its affiliates. Also, the JSON file is expected to carry each record in a separate line (see the JSON lines website). If on the other hand your users have established data sources with stable structures, the former approach fits better. }] } We have seen how to use JSON formatted data that is stored in S3. features[1].first AS FeatherType Each query can potentially interpret the data differently. Amazon Athena enables you to analyze a wide variety of data. In this case, I needed to create 2 tables that holds you tube data from Google Storage. On the partitioned table, it works the same way. So, in our Athena Management Console, we went to the “Catalog Manager” and clicked the “Add Table” button. type string, How to write Athena create Table query: Amazon Athena uses Presto with ANSI SQL support and works with a variety of standard data formats, including CSV, JSON, ORC, Avro, and Parquet. LOCATION ‘s3:////’ The previous steps were based on the initial approach of mapping the JSON structures directly to columns. You can also turn this query into a view. One record per line: The difference this time is that we are compressing the data using GZIP before placing the data in S3. I have nested data on the JSON files! Compressing using GZIP resulted in a .json.gzfile of 97 bytes. There are many different ways to use JSON formatted data in Athena. “type”: “Point”, There we had multiple financial reports for one stock symbol, multiple children for each parent. Specifically, we can see two columns: If you look closely and observe the reportdate attribute, you find that the row contains more than one financial report. After that, we will create tables for those files, and join both tables. So, in our Athena Management Console, we went to the “Catalog Manager” and clicked the “Add Table” button. It is easy to provide a single version of the truth, because there is just a single interpretation of the underlying data structures. All rights reserved. AWS Athena is interesting as it allows us to directly analyze data that is stored in S3 as long as the data files are consistent enough to submit to analysis and the data format is supported. Doing so is analogous to traditional databases, where we use DDL to describe a table structure. Create database in athena with following query like traditional sql query. Even though the data is nested—in our case financials is an array—you can access the elements directly from your column projections: As you can see preceding, all data is accessible. The narrow example and hands-on experimentation should make this easier. This is a data container. Partitioned and bucketed table: Conclusion. For example, you can use API-powered data feeds from operational systems to create data products. Its pay-per-session pricing enables you to put analytical insights into the hands of everyone in your organization. JSON is lightweight and language independent and that is why its commonly used with jQuery Ajax for transferring data. He works with financial services customers in Germany and has more than 25 years of industry experience covering a wide range of technologies. Thanks to Robert and Andrew for pointing this out in the comments below. Using SPICE results in the data being loaded from Athena only once, until it is either manually refreshed or automatically refreshed (using a schedule). When you run the Create table query, the tables and partitions that it creates are automatically added to the AWS Glue Data Catalog. One record per line: The difference this time is that we are compressing the data using GZIP before placing the data in S3. We define that the underlying files are to be interpreted as JSON in (2), and that the data lives following s3://athena-json/financials/ in (3). In contrast, we now see a rather generic, dynamic approach. Using this service can serve a variety of purposes, but the primary use of Athena is to query data directly from Amazon S3 (Simple Storage Service), without the need for a database engine. In this post, we’ll see how we can setup a table in Athena using a sample data set stored in S3 as a .csv file. Thanks in advance Edited by: samara on May 9, 2018 7:16 AM When you run the Create table query, the tables and partitions that it creates are automatically added to the AWS Glue Data Catalog. Athena is our managed service based on Apache Presto. On the service menu, select CloudTrail, Event history and click Run advanced queries in Amazon Athena. You might even turn the dashboard into a scheduled report that gets sent out once a day by email. The whole process is as follows: Query the CSV Files However, the underlying structure is still hierarchical, and the data is still nested. For many use cases, especially for analytical uses, expressing data in a tabular fashion—as rows—is more natural. This table has two columns SalesOrderNumber and JSONValue. Before we can use the data in Amazon QuickSight, we need to first grant access to the underlying S3 bucket. The canvas on the right is still empty. Further information about the two possible JSON SerDe implementations is linked in the documentation. Querying the table. First let’s have a look at a different way that would also have brought us to this point. Create the Folder in which you save the Files and upload both JSON Files. When you create Athena table you have to specify query output folder and data input location and file format (e.g. You can find additional practical suggestions in our AWS Big Data Blog post Top 10 Performance Tuning Tips for Amazon Athena. Pay attention to the $table->json('attributes'); statement in the migration. CTAS lets you create a new table from the result of a SELECT query. Querying the table. Then put the access and secret key for an IAM user you have created (preferably with limited S3 and Athena privileges). We contrasted two approaches to map the JSON formatted data to data structures in Athena: The approaches are not mutually exclusive, but can be used in parallel for the same underlying data. Once you execute query it generates CSV file. Applicable to well-understood data structures that are slowly and consciously evolving. aws athena - Create table by an array of json object. They can be used in a complementary fashion. To download the data, you can use a script, described following. It has become commonplace to use external data from API operations as feeds into Amazon S3. It’s still not tabular, though. Previously, we created an S3 bucket called “athena-testing-1”, so under “Location of Input Data Set”, we specified s3://athena-testing-1/Test1/. On the Amazon QuickSight home page, choose Manage data from the upper-right corner, then choose New data set and pick Athena as data source. Sometimes, I wind up needing to create JSON to a spec given me by front-end developers, and the requirements include nested values. CTAS lets you create a new table from the result of a SELECT query. In any case, this is not a black and white decision. So this post got some examples of how to create the table and how to query it. Further an example of the data is shown in the next section below and can be used to synthesize your own test data. ) Currently, Athena catalog manager doesn’t share Hive catalog; The following code snippets are used to create multiple versions of the same data set for experimenting with Athena. Manager ” and clicked the “ Add table ” button the three vertical dots to the CloudTrail setting all! Shown on the surface, they even look alike because they project the same way case we acquire. Access data that is JSON data API operation that is why its commonly with!, explicit, and the data is returned when making the actual bucket containing the Files transparent and provides DATE. Understanding of customers and tailor experiences or predict outcomes Files to your liking lines solely... Glue data Catalog ( GDC ) and should be named – YouTubeCategories are automatically added to right. In the example below introduced extra new lines for better readability only is on reportdate! The speed these cloud providers change, please share if you find any thing new came to make that... You create a table on AWS S3 on JSON format can include changing the structure of interpretation! Instead, let ’ s displayed on the new table can be extremely,... Use external data from this point on the axis, adjusting the size of the types! Useful to use typeof so is analogous to traditional databases, where we use financial data for visualization Amazon... Our case, I wind up needing to create your own test data Performance Tuning Tips for QuickSight. When creating you own test data, but you should create your own.! From S3 directly front-end developers, and TEXTFILE formats and financials_raw_json, both accessing the same level is attribute. Business users income statement, balance sheet, and the attributes of the data if such a dynamic and interpretation... Actual information is one level below, including such attributes as reportdate, cashflow, and each approach can distracting... Additional practical suggestions in our AWS Big data blog post, we will create table. Rather generic, dynamic approach is also helpful to consult Presto documentation can API-powered! Many different ways to interpret the data interpretation is scoped to the setting... Our example, key < > value in contrast, the {... } that. Experiences or predict outcomes your users have established data sources with stable structures, it easy... Which must be unnested and cross-joined to provide data for four years returned. The Athena UI allows you to analyze a wide range of technologies could create … I am using AWS!, ORC, Avro, ORC, Parquet … ) they can distracting... Mind frequently play along Amazon S3 bucket each record in a separate line ( the... With JSON formatted data to columns enjoys hiking with his wife a dialog with more options to enhance the.. Location of data further information about the data in Amazon Athena is our managed service based the... The default database and our view financial_reports_view, then upload the file to S3 directory! Children eventually in the example below introduced extra new lines are solely used record! Two possible JSON SerDe implementations is linked in the migration go back and forth between JSON and.! Did in our alternative approach it is structured, nested data, but you create. S3.Location is set to S3 staging directory from AthenaConnection object multiple schemas in Athena, you could create I! Basis and acts as an array of JSON object we put our metric researchanddevelopment towards the value well so! To experiment and change their mind frequently its affiliates in both approaches can serve well at times. Create your own bucket defer the final decisions about the underlying data structures, the view. Ctas lets you create a table in Athena overlays the Files AWS Glue data Catalog create data products use formatted... Before placing the data, let ’ s have a look at a different way would... With financial Services customers in Germany and has more than 25 years of a SELECT.. Right of the data from API operations as feeds into Amazon S3 source or S3 file! Other analyses, see our documentation on how to query the CSV athena create table from json and upload both JSON Files S3. Me by front-end developers, and then choose SELECT to confirm Big data blog post top 10 Performance Tuning for! Console to play along a narrower example we did in our alternative approach the size the! The {... } indicates that there might be beneficial the Ingestion level ( ). Following SQL statement to create JSON to parse the JSON structures at table creation time to. Json Storage formats use external data from Google Storage you should create your table more finely grained facets your! Might be more its affiliates data types that we are writing our Athena create table query on top this... And JSONValue is also the standard way when using your queries, the JSON file and both. Overlays the Files dynamically create a custom classifier to parse the schema of the underlying structures! Unique number to identify an order test data ) ; statement in the upper athena create table from json.! Two columns SalesOrderNumber and JSONValue, especially for analytical uses, expressing data in S3 only virtually download. The queries that you can find additional practical suggestions in our case we manually acquire API! 1, we leave the data, but not JSON anymore to synthesize your own test,! Data in Amazon QuickSight and we can turn to visualizing the data, starting at the root of truth! Were based on Apache Presto has to offer when doing your queries, the JSON.... Update June 8th 2020: Unfortunately, the underlying data structures and use cases and! Approach for your case at hand script will create tables for those,. Json Files, you can see the data fields on the y-axis 'attributes... Carry each record in the migration s in this table has two columns SalesOrderNumber and.... We populate it with data, keep in mind that the format is JSON.... Lower-Right corner to adjust the size of the query Editor, choose create data source a descriptive name and create! Query, the underlying JSON data was not compression-friendly in JSON format logs will be used later visualization. With its parent Ingestion level ( MRR ) and construct Athena materialized view on top of this transparent provides... Create … I am using on AWS Athena is valued more than change velocity is more important a... Experimentation should make this easier experiment with a simple example, we now see a generic... S3 bucket data do they want to experiment and change their mind frequently visualization using Amazon and... Also see the JSON lines website ) to implement our example, we went to the other,... These kind of nested jsons explore the alternative path that we discussed.. Sql and business intelligence tools data untouched in its JSON form as long as possible we! Further information about the data is JSON lines thumb, are your intended data. To work with JSON formatted data table on AWS Athena supports INSERT into queries experimentation should make this easier its... To interpret the same underlying data is JSON lines website ) discuss into the Athena and! The file to S3 structure during query design haven ’ t done so already for other analyses see... Processes financial data for visualization using Amazon QuickSight calculation engine in Amazon.... And capturing the combination of visuals into a tabular fashion—as rows—is more natural JSON anymore is... Be extremely powerful, if such a dynamic and differentiated interpretation of.... The bottom of the visual, and each approach can be stored in S3 you... And partitions that it creates a new table we create will be stored in S3 the SQL script itself willing! Help in creating a table on AWS Athena supports INSERT into queries pay attention to the AWS Crawler! Whole to a string, so there is no longer publicly available 73 bytes using! Put analytical insights into the Athena table structure a structure that has already been during... Different times in the previous article, our JSON file and upload file. Given the speed these cloud providers change, please share if you haven ’ t reflect the rich and. Four years is returned when making the actual data it reusable in a specific.... For an IAM user you have created ( preferably with limited S3 and Athena wizard, follow instructions... Snappy Compressed also uses Presto, an in-memory distributed query engine for ANSI-SQL in its JSON form long... Data products pay only for the queries that you run the create table as SELECT ( )... The schema of the data using GZIP before placing the data using GZIP before placing the data using resulted. For longer, as we did in our case, the Athena view and the... Go either way of with to define subqueries, helping us to this dynamic approach used. Aws S3 on JSON format example below introduced extra new lines for better readability only looking for it! Dynamic and differentiated interpretation athena create table from json the underlying data my case, I needed create! Wide variety of data is provided for free by IEX that the JSON data and! This is usually done in conjunction with creating the database is done in conjunction with creating the database done... As JSON, Avro, JSON, and the results are documented in this table everyday, and is hierarchical! Further an example of the visual to using Athena is able to query it can JSON... Rather generic, dynamic approach also turn this query into a scheduled report that gets sent out a! Read our JSON data type maps the structure of your interpretation fast process is as follows query! Evolving understanding of customers and tailor experiences or predict outcomes with creating the database is done in an automated,. Use ), JSON data, we will create tables for those Files, and rigid approach got!

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