How to Leverage Snowflake For Your Data Lake Analytics?
BI Connector Team |
In 2021, it’s quite uncommon to find a successful business without a stable data warehouse in production.
Gone are the days when data warehousing provided a competitive edge for businesses. It is now a necessity for business survival rather than a source of competitive edge.
Many businesses with a data warehouse are focused on creating data lakes to maintain an edge over their competitors in recent years.
In this blog post, we’ll see how to leverage Snowflake for your Data Lake Analytics.
What is Data Lake?
Data Lake, in layman terms, is a repository of all your company’s data stored in their raw format, regardless of their structure.
As businesses grow, the volume of data they generate also grows. Businesses today save all the data generated to a data lake, regardless of their usage in the future.
The data stored in a data lake can be ingested from a data warehouse, data marts, SaaS solutions, or from the Internet of Things (IoT) devices or social media streams.
Data lakes give the freedom to store data literally in any format, even videos, audios, and files in PDF, CSV, JSON, XML, Avro, ORC, Parquet formats, and so on.
Due to its flexibility to store almost any data in its native format in any form – structured, semi-structured, and unstructured, Data Lakes are now an attractive option for many enterprises to store all their data in one place.
No wonder that some data stored could remain unused forever. This fact sparks the curious question, “Then why are many organizations building Data Lakes?”.
Well, it’s for deriving business insights from a vast set of untapped data. One can claim that data warehouses can also help in uncovering hidden insights. But due to their rigidity, data warehouses can store only limited information in a structured, pre-defined manner.
So the data (from a data warehouse) you have for analysis is limited. But in the case of data lake, there is no need to stick to a structure or format for storing data.
Further, data lakes can scale up exponentially as more and more data gets added, and are operated at a much less cost compared to traditional data warehouses.
The organizations are building data lakes for tapping into this advantage and storing all data, to run Machine Learning (ML) models, and BI dashboards to derive strategic business insights.
Due to the massive scaling potential of data lakes, the cloud is a lot more attractive than the on-prem option.
Ok, but how do we maintain the data quality in a data lake, if it’s unstructured and unformatted?
That question makes a lot of sense, let’s see the answer below.
Data Quality Challenges
When a data lake is flooded with data that aren’t categorized properly, then the data is not analysis-ready or insight-friendly.
So data lake without an appropriate data quality setup is simply called a data swamp.
When the data is stored in its raw format, the data must be cataloged based on certain categorizations (like the data source, format, etc).
And this data quality setup must be automated as data lakes get ingested with data, even for the real-time or near real-time data feeds.
Data Lake For Analytics
Data Lakes are used by advanced analytics users such as Data Scientists for building Machine Learning models and uncovering in-depth insights and attributions.
Data Quality is essential for data analytics, regardless of where the data is stored – be it a data lake or a data warehouse.
Since a data lake is highly unstructured, the data scientists must have the option to structure and save data (and automate the process for future use) for their analysis, without making any change to the underlying source data.
Snowflake for Data Lake Analytics
The challenges discussed above, in terms of a Data Lake’s Data Quality for Analytics, can be overcome by setting up your Data Lake in Snowflake.
In this section, we’ll see how to leverage Snowflake for Data Lake Analytics.
Integrate Data from Leading Cloud Platforms
Snowflake is technically a SaaS solution built on top of the big 3 cloud platforms – Microsoft Azure, Amazon Web Services (AWS), and Google Cloud.
Snowflake offers the convenience and flexibility to choose one or more of these leading platforms.
In case, if you already have a data warehouse or data lake built on any of these platforms, you can integrate them easily with Snowflake, and maintain a single source of truth.
The Snowflake Storage Integration feature is used to get the integration done. The feature helps you create and store the identity and access for your external cloud storage.
You can also set up a list of the allowed or blocked storage locations.
Automate Data Ingestion and Transformation
In Snowflake, you can quickly build and run data pipelines and unload them into your data lake.
You can auto-ingest data using Snowpipe, and make use of streams and tasks to set up Change Data Capture (CDC) for pipelines configured for sources with potential real-time or near real-time updates.
Though storing the raw data on an as-is basis is an unwritten norm for Data Lakes, you may also want to transform certain structured and semi-structured data for efficient usage.
Snowflake’s flexible data transformation features enable support to the different combinations of possibilities in the data types and ingestion methods.
The pipelines are extensible and work with external functions and stored procedures.
A lot of frequently-used actions like column reordering, column omission, length enforcement, truncation, etc can be easily executed in Snowflake.
You can make use of Snowsight for quick data validation (and even build and share simple dashboards) before loading them into your data lake.
The automated data ingestion and transformations enhance the analysis-readiness of your data lake.
Enhance Data Security and Data Governance
Data Security is a big challenge in Data Lakes. With such a diverse set of data, it is extremely hard to control who can access what.
Snowflake helps set up your Data Security and Data Governance at the macro-level for your Data Lake.
In Snowflake, you can easily set up Role-based Access Control, row/column/object level restrictions, Multi-Factor Authentication (MFA), SSO, internal/external data sharing rules, etc for your Data Lake.
Further, with Snowflake, you are better equipped to achieve the critical regulatory compliances such as HIPAA, SOC1, SOC2, etc.
Fast Query Performance
When a large set of unformatted, multi-structured data (in a data lake) is queried by multiple users at the same time, the performance by default takes a hit.
With Snowflake, you can easily navigate such scenarios.
Snowflake comes with Massively Parallel Processing (MPP) capabilities, to help you achieve faster query performance even when multiple users are querying (simple or complex queries) your data lake at the same time.
With MPP, the users can even execute a near-unlimited number of complex queries concurrently at lightning speeds.
Additionally, Snowflake helps users with reusable results for already executed queries on the external tables. These results are called Materialized Views (MV) in Snowflake.
The users can simply utilize the materialized views rather than creating or running a new query again.
Any changes to the data in the materialized views are automatically updated by Snowflake’s background services. The users can eliminate the manual refresh process.
Minimize Storage Costs
Data Lakes can massively scale up with ease but also costs you proportionately based on the volume of data stored.
Hence, an efficient storage strategy is essential to trim the costs. Snowflake provides various options to save your storage space and save costs.
In Snowflake, you can set up space optimization algorithms and rules for different data sources and formats.
Data compression options are also available.
With Snowflake in the tech arsenal, building and managing a secure, cost-effective Data Lake (without performance issues) is not a painful task anymore.
Snowflake also offers organizations with the simplicity and flexibility to maintain data security and data quality required for Data Lake Analytics.
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