According to a study by KPMG, for every $1B invested in the US, $122M was wasted due to lacking project performance. Project management software like Hive is helping businesses solve this problem. But, you are not leveraging the platform to its full potential if you are not analyzing the data out of it.
You can derive many insights from replicating the data into a data warehouse like Snowflake. It includes improved customer satisfaction by examining client interactions and centralizing data from all sources. In this blog, I will take you through three methods you can use for data integration. I will also explain the main benefits of data replication from Hive to Snowflake.
Let’s get started!
Method 1: Connecting Hive to Snowflake by Using CSV Files
Export Data into CSV Files
Depending on the version of Hive, there are two ways to implement this method for Hive to Snowflake migration.
For Hive version 11 or higher, use the following command:
ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘,’ dictates that the columns should be delimited by a comma.
INSERT OVERWRITE LOCAL DIRECTORY '/home/hirw/sales
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
select * from sales_table;
For Hive versions older than 11:
Although you need a comma-separated file, writing to a file by default after picking the Hive table will result in a tab-separated file.
hive -e 'select * from sales_table' > /home/hirw/sales.tsv
You can choose a table and pipe the results to sed while also passing a regex expression by using the code below.
hive -e 'select * from sales_table' | sed 's/[\t]/,/g' > /home/hirw/sales.csv
The regex expression matches every tab character ([t]) globally and replaces it with a ‘,’.
Load CSV files in Snowflake
- Step 1: Choose the database where you want to upload the files after logging onto your Snowflake Account. To create a named file format for CSV, use the Create or Replace FILE FORMAT command.
use database test_db;
create or replace file format new_csv_format
type = csv
field_delimiter = ','
skip_header = 1
null_if = ('NULL', 'null')
empty_field_as_null = true
compression = gzip;
- Step 2: Use the create or replace table command to build the new table if the target table doesn’t already exist.
CREATE OR REPLACE TABLE test_students (
- Step 3: Use the PUT command to load the CSV file into Snowflake’s staging area.
put file://D:\test_stud.csv @test_db.PUBLIC.%test_students;
Step 5: Load the data into your target table using the COPY INTO command.
copy into test_students
file_format = (format_name = 'new_csv_format' , error_on_column_count_mismatch=false)
pattern = '.*test_stud.csv.gz'
on_error = 'skip_file';
- Step 4: Use the COPY INTO command to load the data into your target table.
copy into test_students
file_format = (format_name = ‘new_csv_format’ , error_on_column_count_mismatch=false)
pattern = ‘.*test_stud.csv.gz’
on_error = ‘skip_file’;
That’s about it. Let’s have a look at some use cases where this method of Hive Snowflake migration is ideal:
- One-time data replication: The manual labor and time required are justified when your business teams want this Hive data only once every quarter, year, or other specified period.
- No data transformation necessary: This method offers few possibilities for data transformation. It is therefore preferable if the data in your spreadsheets is precise, standardized, and offered in a format that is suitable for analysis.
- A smaller number of files: It takes a lot of effort to download and write SQL queries to upload several CSV files. If you have to create a 360-degree view of the company, it can be very tedious.
Method 2: Building Data Pipelines
In this method, Hive to Snowflake integration is done by building data pipelines. You can use Kafka as the streaming platform.
Kafka works in two ways:
- Self-managed(either using own servers/own cloud machines)
- Managed by Confluent(a company that created Kafka)
If the connector is not available, you can easily use any programming language and build a connector. In this case, ready-made connectors are available for both Hive and Snowflake.
So, the steps involved in the method are:
Although it sounds very useful, it has some disadvantages which are:
- Maintaining the Kafka cluster is not easy.
- The whole process takes away a large chunk of your data engineering efforts which could otherwise go into other high-priority tasks.
- Maintaining the pipeline is a tedious task.
Do you feel that you need a better method? Cool. Let me introduce you to one that resolves the drawbacks of methods one and two.
Method 3: Using an Automated Data Pipeline
Here, you can use a third-party tool for Hive to Snowflake migration using an automated data pipeline.
The benefits are:
- Identify patterns and reuse: Automated data pipelines help you to see the patterns in the wider architecture by looking into pipes as their examples. These identified patterns can be reused and repurposed for other data flows as well when you replicate data from Hive to Snowflake.
- Quickly integrate new data sources: An automated data pipeline will enable you to have a fair understanding of how data flows through the systems. That will help you easily add new data sources along with Hive to your data stack. And, this reduces the time and cost of their integration.
- Provides better security during data replication: Data security during Hive to Snowflake data replication is built from identifiable patterns and an understanding of tools and architectures. This enables the reuse of these patterns for all new dataflows and data sources for better security.
- Allows incremental build: Your Hive data flows can be grown gradually when the data flows are considered pipelines.
- Provides flexibility and agility: You will have better flexibility to any changes in the Hive data flow like sources or your customers’ needs.
The benefits are tempting you to opt for this method, right? The easy steps to configure this are even more tempting. Here you go.
Step 1: Configure Hive as a source
Step 2: Configure Snowflake as the destination
Next, let’s look at the benefits of replicating data from Hive to Snowflake.
What Can You Achieve by Replicating Data from Hive to Snowflake?
- You can centralize your business data: You can develop a single customer view using data from your business to evaluate the effectiveness of your teams and initiatives.
- You will get in-depth customer insights: To understand the customer journey and provide insights that may be applied at different points in the sales funnel, combine all of the data from all channels.
- You can improve customer satisfaction: Examine client interactions during project management. Using this information along with consumer touchpoints from other channels, identify the variables that will increase customer satisfaction.
That’s it about the benefits of connecting Hive to Snowflake for data replication. Let’s wrap up!
Hive to Snowflake data integration helps businesses in many ways. It gives you more insights into your team’s efficiency. The data migration also helps to analyze customer interactions and use the data to improve customer satisfaction.
There are three ways to achieve Hive to Snowflake replication. Using CSV files is one of the methods, which can be used for small files, and when no data transformation is needed. The second method is by using the Kafka streaming platform.
This requires a lot of bandwidth from the data engineering team. The third option available is relying on a fully automated data pipeline to replicate data from Hive to Snowflake. This will save a lot of your time and effort otherwise put into this method. So, look into your requirements and decide which one is best suitable for you.
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