Data loading is a surmountable task for organizations all over the world. While several platforms make this task easier, several data loading issues surface regularly. Amazon’s Redshift is a popular choice for data loading in an organized manner. In this blog, you will learn how to perform Redshift Incremental Load.
Several methodologies and techniques need to be applied right for this process to benefit your data management. When loading data into Amazon Redshift, in an ideal scenario, each slice should be handling an equal amount of workload. When the data is loaded from single large files, often an uneven split up is likely to take place, thus slowing down the process.
This is where Redshift Incremental Loading can be beneficial with a uniform data pipeline working for the loading process. This article deals with the different facets of incremental data loading via Redshift along with the benefits of doing so. You will also read about the methods to load this data incrementally and some common challenges in the process.
Table of Contents
Introduction to Redshift
Amazon Redshift is Amazon’s Cloud-based Data Warehouse service that is a completely managed platform under Amazon Web Services. It is a popular choice for organizations to collect and store their data for efficient analysis. Loading data efficiently is the first step to being able to employ other tools to gain valuable insights for your business.
The merits of Amazon Redshift can be seen in various aspects. A primary benefit is seen in processing frequent queries faster. This query optimization process in Redshift allows faster execution of consequent frequently processed queries, thus, making it an effective channel for data processing. It also has a structured architecture that enables parallel processing and reduces the time taken for data loading significantly.
Various integral data management aspects can be easily implemented using Redshift as your primary service. Some significant advantages of using Redshift for data loading include:
- Amazon Redshift is a petabyte-scale data warehouse service. As a cloud-based platform, it provides a convenient service that can be easily scaled up. You can initiate with data usage of some gigabytes and eventually scale-up to a petabyte. Thus, Redshift can prove to be effective to utilize all of your data for valuable business and marketing insights.
- With Amazon Redshift, not only quick scaling can be facilitated but an efficient workload distribution can be implemented. You can employ essential features such as data restoration, cluster allocation, and several integrations with data warehouse applications.
- Having an Amazon Redshift setup, there can often be issues loading data from the existing warehouse. With non-uniform data spurts, there can be several inconsistencies and inaccuracies with the data that ends up being loaded. This is where Redshift Incremental Loading plays an essential role.
To explore more about Amazon Redshift, visit here.
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It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. It allows you to focus on key business needs and perform insightful analysis using various BI tools.
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What is Incremental Load?
Incremental data loading refers to an incremental update on the data sets to accommodate all pointers of incoming data. This makes the data load uniform and evenly streamed even for bulk data bunches.
Typically, in the case of a full-loading process, the entire dataset is collected and processed at once, coming from the source system. This leaves little to no room for any modifications, real-time changes, or frequent alterations, since the process of loading bulk data repeatedly with changes can be pretty cumbersome. These tasks are made possible by incrementally loading datasets instead of a full bulk load. One of the best Incremental Data Loading platforms is Redshift Incremental Loading.
Significance of Performing Redshift Incremental Load
There are other scenarios where full loading might prove to be ineffective. For instance, in the case of very large datasets, full loading isn’t employable. There might be issues regarding processing queries for such a large set from the source system. It might also be difficult to load this data within a given period.
In other instances, businesses working with real-time updates and changes might face redundancy with full loading methods. This leads all their data to be removed at once, making it difficult to incorporate updates and real-time changes for updated business insights.
This is where Redshift Incremental Loading becomes crucial. The data records cannot be removed when Amazon Redshift Incremental Loading methods are implemented. This makes them available for integrations with incoming datasets that might be relevant. While in the case of a full load, the records of the dataset are removed and completely absent from the updated dataset, Redshift Incremental Load offers a plausible solution to such loopholes.
Methods to Perform Redshift Incremental Load
While there are several methods how you can use Redshift Incremental Load to load data from your Data Warehouse or from another table to Redshift, here are a few channels you can use:
1. Build an Independent ETL Pipeline
With Redshift’s unique architecture, you can build an independent Extract-Transform and Loading pipeline.
Step 1: Start by using the COPY command to bulk load data into tables from evenly sized files, optimally between 1MB and 1GB. Load your data from your host warehouse using manifest files to specify the data files. You can run the following commands to hold data in temporary staging tables from the host:
ALTER TABLE APPEND | CREATE TABLE AS | INSERT INTO.
Step 2: Run multiple SQL queries to change data to its final form. Your queries would look similar to:
CREATE temporary staging_table;
INSERT INTO staging_table SELECT .. FROM source (transformation logic);
DELETE FROM daily_table WHERE dataset_date =?;
INSERT INTO daily_table SELECT .. FROM staging_table (daily aggregate);
DELETE FROM weekly_table WHERE weekending_date=?;
INSERT INTO weekly_table SELECT .. FROM staging_table(weekly aggregate);
Step 3: Configure slots for workload management.
Step 4: Use the UNLOAD command to extract files. Here you can SELECT the nodes and clusters for parallel incremental loading of multiple files.
2. Use AWS Glue
If you do not wish to build your own ETL pipeline, you can use AWS Glue that extends a completely managed ETL service. Here, you can set up an AWS data pipeline that is readily generated by Glue.
AWS Glue will automatically render data models and schema to generate scripts for incremental data loading. With an automatic schema discovery, code generation, and automated partitioning of queries, Glue makes it a lot easier to schedule an Amazon Redshift Incremental Loading process.
The only thing to note is whether the cost incurred falls within budget as AWS Glue renders services at $0.44 per digital processing unit hour with additional costs for the partitioning data catalog and other features. You can also use other third-party Redshift ETL tools for a similar approach. They offer a pre-integrated interface with different source platforms, thus, you can consider a tool as per your data sourcing and other specifics.
Thus, you can use any of the above-mentioned methods to implement Redshift Incremental Loading. While you might be inclined towards using a custom script if you are code savvy, there are some challenges that you might encounter with manually monitored data loading.
You can use Hevo, to aid the data loading process with automation and a fully managed platform. Hevo is a no-code platform that is compatible with several data warehouses and platforms for easy integration for Redshift Incremental Loading.
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