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    Load Data into BigQuery: Easy Step

    In this article, you will learn how to load data into BigQuery, and explore some different data type uploads to the Google Big Query Cloud Storage.

    Load Data into BigQuery: Easy Step-by-Step Guide

    Muhammad Faraz • September 1st, 2020

    Are you struggling to load data into BigQuery? Are you confused, which is the best method to load data into BigQuery? If yes, then this blog will answer all your queries. In this article, you will learn how to load data into BigQuery, and explore some different data type uploads to the Google Big Query Cloud Storage, including CSV and JSON files. You will also learn about the ways of uploading through an API or add-on. If you need to analyze terabytes of data in a few seconds, Google BigQuery is the most affordable option.

    Let’s see how this blog is structured for you:

    What is Google BigQuery?

    Types of Data Load in BigQuery

    Data Ingestion Format

    Load Data into BigQuery

    Upload Data from CSV File

    Upload Data from JSON Files

    Upload Data from Google Cloud Storage

    Upload Data from Other Google Services

    Download Data with the API


    What is Google BigQuery?

    Google BigQuery is serverless, highly scalable, and cost-effective multi-cloud data warehouse designed for business agility.

    Here are few features of Google BigQuery:

    BigQuery allows us to analyze petabytes of data at a quick speed with zero operational overhead.

    No cluster deployment, no virtual machines, no setting keys or indexes, and no software are required.

    Stream millions of rows per second for real-time analysis.

    Thousands of cores are used per query.

    Separate storage and computing.

    To understand more about Google BigQuery, please refer to the following Hevo Data article.

    Hevo Data: Migrate your Data Seamlessly

    Hevo is a No-code Data Pipeline that helps you to transfer data from 100+ data sources to BigQuery. It is a fully-managed platform that automates the process of data migration. It also enriches the data by transforming it into an analysis-ready form. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. It also provides a consistent and reliable solution to manage data in real-time.

    Let’s discuss some unbeatable features of Hevo:

    Fully Managed: It requires no maintenance as Hevo is a fully automated platform.Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.Fault-Tolerant: Hevo is capable of detecting anomalies in the incoming data and informs you instantly. All the affected rows are kept aside for correction so that it doesn’t hamper your workflow.Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis.Schema Management: Hevo can automatically detect the schema of the incoming data and maps it to the destination schema.Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within pipelines.Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support call.

    Give Hevo a try by signing up for a 14-day free trial today.

    Types of Data Load in BigQuery

    Following types of data loads are supported in Google BigQuery:

    You can load data from cloud storage or a local file. The supported records are in the Avro, CSV or JSON format.

    Data exports from Firestore and Datastore can be uploaded into Google BigQuery.

    You can load data from other Google Services such as Google Ads Manager and Google Analytics.

    Streaming inserts can be actively loaded in BigQuery. You can read more about it on this link.

    Data Manipulation Language (DML) statements are also used for bulk data upload.

    Data uploading through Google Drive is NOT yet supported, but data can be queried in the drive using an external table.

    @[email protected]#=img=#

    Download the Cheatsheet on How to Set Up High-performance ETL to BigQuery

    Learn the best practices and considerations for setting up high-performance ETL to BigQuery

    Data Ingestion Format

    Proper Data Ingestion format is necessary to carry out a successful upload of data. The following factors play an important role in deciding the data ingestion format:

    Schema Support: One important feature of BigQuery is that it creates a table schema automatically based on the source data. Data formats like Avro, ORC, and Parquet are self-describing formats. No specific schema support is needed for these, but for data formats like JSON and CSV, an explicit schema can be provided.Flat Data/Nested and Repeated Fields: Nested and Repeated data helps in expressing hierarchical data. All the formats including Avro, ORC, Parquet, Firestore exports, support data with Nested and Repeated Fields.Embedded Newlines: When data is being loaded from JSON files, the rows need to be newline delimited. Query expects newline-delimited JSON files to contain a single record per line.

    sumber : hevodata.com

    Introduction to loading data

    Introduction to loading data

    This document provides an overview of loading data into BigQuery.


    There are several ways to ingest data into BigQuery:

    Batch load a set of data records.

    Stream individual records or batches of records.

    Use queries to generate new data and append or overwrite the results to a table.

    Use a third-party application or service.

    Batch loading

    With batch loading, you load the source data into a BigQuery table in a single batch operation. For example, the data source could be a CSV file, an external database, or a set of log files. Traditional extract, transform, and load (ETL) jobs fall into this category.

    Options for batch loading in BigQuery include the following:

    Load jobs. Load data from Cloud Storage or from a local file by creating a load job. The records can be in Avro, CSV, JSON, ORC, or Parquet format.SQL. The LOAD DATA SQL statement loads data from one or more files into a new or existing table. You can use the LOAD DATA statement to load Avro, CSV, JSON, ORC, or Parquet files.BigQuery Data Transfer Service. Use BigQuery Data Transfer Service to automate loading data from Google Software as a Service (SaaS) apps or from third-party applications and services.BigQuery Storage Write API. The Storage Write API lets you batch-process an arbitrarily large number of records and commit them in a single atomic operation. If the commit operation fails, you can safely retry the operation. Unlike BigQuery load jobs, the Storage Write API does not require staging the data to intermediate storage such as Cloud Storage.Other managed services. Use other managed services to export data from an external data store and import it into BigQuery. For example, you can load data from Firestore exports.

    Batch loading can be done as a one-time operation or on a recurring schedule. For example, you can do the following:

    You can run BigQuery Data Transfer Service transfers on a schedule.

    You can use an orchestration service such as Cloud Composer to schedule load jobs.

    You can use a cron job to load data on a schedule.


    With streaming, you continually send smaller batches of data in real time, so the data is available for querying as it arrives. Options for streaming in BigQuery include the following:

    Storage Write API. The Storage Write API supports high-throughput streaming ingestion with exactly-once delivery semantics.Dataflow. Use Dataflow with the Apache Beam SDK to set up a streaming pipeline that writes to BigQuery.BigQuery Connector for SAP. The BigQuery Connector for SAP enables near real time replication of SAP data directly into BigQuery. For more information, see the BigQuery Connector for SAP planning guide.

    Generated data

    You can use SQL to generate data and store the results in BigQuery. Options for generating data include:

    Use data manipulation language (DML) statements to perform bulk inserts into an existing table or store query results in a new table.

    Use a CREATE TABLE ... AS statement to create a new table from a query result.

    Run a query and save the results to a table. You can append the results to an existing table or write to a new table. For more information, see Writing query results.

    Third-party applications

    Some third-party applications and services provide connectors that can ingest data into BigQuery. The details of how to configure and manage the ingestion pipeline depend on the application. For example, to load data from external sources to BigQuery's storage, you can use Informatica Data Loader or Fivetran Data Pipelines. For more information, see Load data using a third-party application.

    Choosing a data ingestion method

    Here are some considerations to think about when you choose a data ingestion method.

    Data source. The source of the data or the data format can determine whether batch loading or streaming is simpler to implement and maintain. Consider the following points:

    If BigQuery Data Transfer Service supports the data source, transferring the data directly into BigQuery might be the simplest solution to implement.

    If your data comes from Spark or Hadoop, consider using BigQuery connectors to simplify data ingestion.

    For local files, consider batch load jobs, especially if BigQuery supports the file format without requiring a transformation or data cleansing step.

    For application data such as application events or a log stream, it might be easier to stream the data in real time, rather than implement batch loading.

    Slow-changing versus fast-changing data. If you need to ingest and analyze data in near real time, consider streaming the data. With streaming, the data is available for querying as soon as each record arrives. Avoid using DML statements to submit large numbers of individual row updates or insertions. For frequently updated data, it's often better to stream a change log and use a view to obtain the latest results. Another option is to use Cloud SQL as your online transaction processing (OLTP) database and use federated queries to join the data in BigQuery.

    If your source data changes slowly or you don't need continuously updated results, consider using a load job. For example, if you use the data to run a daily or hourly report, load jobs can be less expensive and can use fewer system resources.

    sumber : cloud.google.com

    4. Loading Data into BigQuery

    Chapter 4. Loading Data into BigQuery In the previous chapter, we wrote the following query: SELECT state_name FROM `bigquery-public-data`.utility_us.us_states_area WHERE ST_Contains( state_geom, ST_GeogPoint(-122.33, 47.61)) We also learned that the city … - Selection from Google BigQuery: The Definitive Guide [Book]

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    Google BigQuery: The Definitive Guide by Valliappa Lakshmanan, Jordan Tigani

    Google BigQuery: The Definitive Guide by Valliappa Lakshmanan, Jordan Tigani Chapter 4. Loading Data into BigQuery

    In the previous chapter, we wrote the following query:

    SELECT state_name

    FROM `bigquery-public-data`.utility_us.us_states_area

    WHERE ST_Contains( state_geom,

    ST_GeogPoint(-122.33, 47.61))

    We also learned that the city at the location (-122.33, 47.61) is in the state of Washington. Where did the data for the state_name and state_geom come from?

    Note the FROM clause in the query. The owners of the bigquery-public-data project had already loaded the state boundary information into a table called us_states_area in a dataset called utility_us. Because the team shared the utility_us dataset with all authenticated users of BigQuery (more restrictive permissions are available), we were able to query the us_states_area table that is in that dataset.

    But how did they get the data into BigQuery in the first place? In this chapter, we look at various ways to load data into BigQuery, starting with the basics.

    The Basics

    Data values such as the boundaries of US states change rarely,1 and the changes are small enough that most applications can afford to ignore them. In data warehousing lingo, we call this a slowly changing dimension. As of this writing, the last change of US state boundaries occurred on January 1, 2017, and affected 19 home owners and one gas station.2

    State boundary data is, therefore, the type of data that is often loaded just once. Analysts query the single table and ignore the fact that the data could change over time. For example, a retail firm might care only about which state a home is in currently to ensure that the correct tax rate is applied to purchases from that home. So when a change does happen, such as through a treaty between states or due to a change in the path of a river channel, the owners of the dataset might decide to replace the table with more up-to-date data. The fact that queries could potentially return slightly different results after an update compared to what was returned before the update is ignored.

    Ignoring the impact of time on the correctness of the data might not always be possible. If the state boundary data is to be used by a land title firm that needs to track ownership of land parcels, or if an audit firm needs to validate the state tax paid on shipments made in different years, it is important that there be a way to query the state boundaries as they existed in years past. So even though the first part of this chapter covers how to do a one-time load, carefully consider whether you would be better off planning on periodically updating the data and allowing users of the data to know about the version of the data that they are querying.

    Loading from a Local Source

    The US government issues a “scorecard” for colleges to help consumers compare the cost and perceived value of higher education. Let’s load this data into BigQuery as an illustration. The raw data is available on catalog.data.gov. For convenience, we also have it available as 04_load/college_scorecard.csv.gz in the GitHub repository for this book. The comma-separated values (CSV) file was downloaded from data.gov and compressed using the open source software utility gzip.


    Why did we compress the file? The raw, uncompressed file is about 136 MB, whereas the gzipped file is only 18 MB. Because we are about to send the file over the wire to BigQuery, it makes sense to optimize the bandwidth being transferred. The BigQuery load command can handle gzipped files, but it cannot load parts of a gzipped file in parallel. Loading would be much faster if we were to hand BigQuery a splittable file, either an uncompressed CSV file that is already on Cloud Storage (so that the network transfer overhead is minimized) or data in a format such as Avro for which each block is internally compressed but the file as a whole can be split across workers.

    A splittable file can be loaded by different workers starting at different parts of the file, but this requires that the workers be able to “seek” to a predictable point in the middle of the file without having to read it from the beginning. Compressing the entire file using gzip doesn’t allow this, but a block-by-block compression such as Avro does. Therefore, using a compressed, splittable format such as Avro is an unmitigated good. However, if you have CSV or JSON files that are splittable only when uncompressed, you should measure whether the faster network transfer is counterbalanced by the increased load time.

    From Cloud Shell, you can page through the gzipped file using zless:

    zless college_scorecard.csv.gz


    Here are detailed steps:

    Open Cloud Shell in your browser by visiting https://console.cloud.google.com/cloudshell.

    In the terminal window, type: git clone https://github.com/GoogleCloudPlatform/bigquery-oreilly-book.

    Navigate to the folder containing the college scorecard file: cd bigquery-oreilly-book/04_load.

    Type the command zless college_scorecard.csv.gz, and then use the space bar to page through the data. Type the letter q to quit.

    The file contains a header line with the names of the columns. Each of the lines following the header contains one row of data.

    sumber : www.oreilly.com

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