Firebolt Data Warehouse Simplified 101

on Data Aggregation, Data Extraction, Data Modelling, Data replication, Data Warehouse, Data Warehouses • July 20th, 2021

Firebolt data warehouse

Today, organizations generate huge volumes of data, across verticals, every day. This has led to an increased demand for Data Storage options. Cloud Storage was invented to meet this demand. Organizations enjoy a number of benefits when they store their data in the Cloud compared to when they use On-premise storage options. Data stored in the Cloud is easy to access from any location. At the same time, the Cloud offers massive scalability compared to On-premise storage options. 

Many businesses are opting for Cloud Storage compared to other storage options. The number of Cloud Storage providers is rising at a high rate. The Firebolt Data Warehouse is a good example of one of the most highly anticipated new Cloud Storage providers. It is well-known for its ease of use and fast speed when it comes to analyzing data. In this article, we will be discussing the Firebolt Cloud Data Warehouse in detail. 

Table of Contents

What is Firebolt Data Warehouse?

Firebolt Data Warehouse: Firebolt logo
Image Source

Firebolt is a Cloud Data Warehousing solution that helps its users streamline their Data Analytics and access to insights. It offers fast query performance and combines Elasticity, Simplicity, Low cost of the Cloud, and innovation in Analytics. It is developed with a powerful SQL Query Engine that separates Computing and Storage, enabling users to spin up many isolated resources on a similar database. 

Businesses that use Firebolt’s Data Warehouses, are able to achieve Petabyte-scale high-performance and interactive Data Analytics, enabling employees and analysts to analyze huge volumes of data and improve the ROI for data collection. 

The Firebolt Data Warehouse comes with all that you need to give your users an unbelievable data experience. It is 4-6000x faster than other Cloud Data Warehouse providers like Snowflake, Athena, Amazon Redshift, and others. 

Note that there are no impossible data challenges with the Firebolt Database. It is suitable for aggregating data that lacks granularity. If you need to make changes to your schema frequently, make your Semi-Structured data ready for analysis, or your queries are too slow even after optimizing them, choose Firebolt Cloud Data Warehouse. It turns all impossible data problems into easy everyday tasks. 

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Hevo Data, a No-code Data Pipeline helps to load data from any data source such as SaaS applications, Cloud Storage, SDKs, and Streaming Services to a destination of your choice such as Firebolt and simplifies the ETL process. It supports 100+ data sources (including 40+ free data sources) and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. Hevo not only loads the data onto the desired Data Warehouse/destination but also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.

Its completely automated Data Pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different BI tools as well.

Check out why Hevo is the Best:

  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
  • Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
  • Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
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Steps to use Firebolt Data Warehouse

A) Creating a New Database in Firebolt

To work with your data on Firebolt, you should first create a new Database and Engine. The Firebolt Engine will represent the computing resources attached to a Database for a particular workload. There are different types of engines that you can select depending on the workflow that you need to manage. The engines can be scaled either upwards or downwards even after the initial configuration. 

The following steps can help you to create a new Database and Engine in Firebolt:

Step 1: Open the Database Page

  • Open the Database Page in Firebolt and click “+ New Database”.
  • Give the Database the name “Tutorial” and select the region. 
Firebolt Data Warehouse: Create New Database

Step 2: Create the Database and Engine

  • Click the “Create Database” button.
  • This will create both the Database and the Engine.

Step 3: Start the Database Engine

  • Open the Engines’ page and find the “Tutorial_ingest” Engine from the list of available Engines.
  • Click “Start” to start the Engine. When the Engine starts running, the status will change to “On”. 

Step 4: Query the Database

  • Open the SQL Workspace Page. You will be prompted to select the Database that you need to perform the query on. 

B) Importing Data into Firebolt Database

To use your data in Firebolt, you must establish a connection with your data sources and then ingest the data. Follow the steps given below to do so:

Step 1: Create an External Table

You should now connect to a public Amazon S3 bucket (data source) where your Parquet Files are stored. Use the Firebolt Demo Bucket which has tables from the TPC-H benchmark. 

But first, create an External Table, which is a virtual table that establishes a direct connection to an external data source from the Amazon S3 bucket without the need to load the data into a Firebolt Table

Paste the command given below into the SQL Workspace page to create an External Table:

CREATE EXTERNAL TABLE IF NOT EXISTS ex_lineitem
(       l_orderkey              LONG,
        l_partkey               LONG,
        l_suppkey               LONG,
        l_linenumber            INT,
        l_quantity              LONG,
        l_extendedprice         LONG,
        l_discount              LONG,
        l_tax                   LONG,
        l_returnflag            TEXT,
        l_linestatus            TEXT,
        l_shipdate              TEXT,
        l_commitdate            TEXT,
        l_receiptdate           TEXT,
        l_shipinstruct          TEXT,
        l_shipmode              TEXT,
        l_comment               TEXT
)

URL = 's3://firebolt-publishing-public/samples/tpc-h/parquet/lineitem/'
-- CREDENTIALS = ( AWS_KEY_ID = '******' AWS_SECRET_KEY = '******' )

OBJECT_PATTERN = '*.parquet'
TYPE = (PARQUET);

The command will create an External Table named ex_lineitem which will be shown in the Object Panel of the Database.

Step 2: Import the Data into External Table

It’s now time to load the data into the Firebolt Database. For that, you need to create a Fact Table named lineitem and load it with data so as to work with the data from the ex_lineitem table. 

Use the code given below to create a new Fact Table:

CREATE FACT TABLE IF NOT EXISTS lineitem
(       l_orderkey              LONG,
        l_partkey               LONG,
        l_suppkey               LONG,
        l_linenumber            INT,
        l_quantity              LONG,
        l_extendedprice         LONG,
        l_discount              LONG,
        l_tax                   LONG,
        l_returnflag            TEXT,
        l_linestatus            TEXT,
        l_shipdate              TEXT,
        l_commitdate            TEXT,
        l_receiptdate           TEXT,
        l_shipinstruct          TEXT,
        l_shipmode              TEXT,
        l_comment               TEXT
) PRIMARY INDEX l_orderkey, l_linenumber;

The table will be created and shown in the Object Panel of the Database. 

We can now use the INSERT INTO command to copy data from the external table into the Fact Table:

INSERT INTO lineitem
SELECT *
FROM   ex_lineitem;

When you query the lineitem table, it should have data in it. 

Limitations of using Firebolt Cloud Data Warehouse

The following are the limitations of the Firebolt Data Warehouse:

  • Steep Learning Curve: It is a complex Data Warehouse. Hence, it may take a long time for one to learn how to use it. 
  • Technical knowledge is Required: Using this complex Data Warehouse requires you to write and run SQL queries. This means that knowledge of SQL will be needed. 
  • Real-time Data Transfer: Firebolt users encounter challenges when they need to transfer data from and to the Firebolt Database in real-time. 

Conclusion

Today, Data Warehouses are a major part of Data Storage and Processing Needs. With the emergence of companies that want to benefit from Data Storage facilities, it is better to incorporate Data Warehouses for accelerating Data Analytics. A Data Warehouse can simplify the access to quality data for both experts and non-experts. However, if an organization is leveraging only Structured data, a Data Warehouse would be the most beneficial option.

In this article, you learned about the key concepts associated with Firebolt Data Warehouse. You also learned about the necessary steps required to use this new Data Warehouse for your data and analytical needs.

In case you want to integrate data from data sources into your desired Data Warehouse/destination such as Firebolt and seamlessly visualize it in a BI tool of your choice, then Hevo Data is the right choice for you! It will help simplify the ETL and management process of both the data sources and destinations.

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Share your experience of learning about Firebolt Data Warehouse in the comments section below.

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