Redshift versus Snowflake

We are going to go over a couple of major areas to inform you whether Redshift or Snowflake is a better data warehouse for your business is based on three categories security, performance, pricing needs.

How do these two cloud data warehouse compare to one another.

  • Deep discounts when your commitment is for a longer term
  • More unified offer package
  • Security and compliance enforced in a thorough manner for all users
  • Machine learning engine can be easily attached to
  • Little more hands-on maintenance
  • Snowflake:

  • Pay separately for compute and storage
  • More robust support for JSON-based functions
  • Tier-based packages
  • Security and compliance options will be different by tier
  • Unique architecture designed to scale on the web
  • More automated database maintenance features
  • Redshift is a solid cost-efficient solution for enterprise-level implementations. Snowflake is a good warehouse to start and grow with. If your business has less experience resources then Snowflake might be a good start for your business, where as if you have experience resources in this are Redshift would be a great warehouse for your company.

    Security: Choose your warehouse wisely
    While Redshift addresses security and compliance in a very thorough manner, Snowflake takes a more subtlety approach.

    Redshift’s encryption from start to finish can be tailored to fit anyone’s security requirements. Redshift can also be isolated within the network by being placed in a virtual private could (VPC) and then linked to an existing infrastructure (VPN). Another nice feature that can help your businesses to meet their compliance requirements with Auditing is integrating Redshift with AWS CloudTrail. The wealth of logs and analytics that you can receive will help you in the long run as far as debugging issues and shed light on performance issues.

    Snowflake handles end-to-end encryption automatically encrypting the data in transport and at rest. You are able to isolate your Snowflake with options VPC/VPN. A big difference in security and compliance from Snowflake to Redshift is that options for this grows stronger on which edition of Snowflake you opt for. This is where you have to carefully consider with edition of Snowflake will cover your needs.

    Performance: New Redshift features compete with Snowflake

    Snowflake and Redshift both utilize columnar storage (this is where data is stored by columns and not by rows) and parallel processing (this is computing that separate parts of the overall tasks are broken up) for simultaneous processing, which will save your analytical team a lot of time when processing very large jobs.

    Snowflake articulates that its performance is driven by its architecture that supports structured and semi-structured data. It places the storage, compute and cloud services separately to optimize their independent performance.

    Both Redshift and Snowflake offer concurrency-scaling (adds and removes computational capacity to handle ever-changing demand) features and machine learning to really add value to their warehouses. Both warehouses also offer free trails to their products to help companies experience their solutions value first hand.

    Pricing: Don’t stop at the sticker price but also consider long-term benefits
    Both warehouses offer on-demand pricing, but bundle associated features differently to really separate themselves from one or the other.

    The differences

  • Snowflake separates compute usage from storage in their pricing structures.
  • AWS Redshift offers users a dedicated daily amount of concurrency scaling and once usage is exceeded Redshift charges by the second.
  • Snowflake automatically includes concurrency scaling.
  • Redshift gloats the potential for deep discounts over the long term if you commit to a one or three year contract. Redshift does offer a option to pay and hourly rate.
  • Snowflake offers five options with additional features tied to each increasing the level of price.
  • When you are trying to make your final decision on which of the two warehouse to go with make sure you look at what you need specifically data volume, processing power and analytically requirements. Look for the right warehouse that will improve your accuracy and speed of data-driven decisions. Also you need to look at the resources that you have inside your business to ensure that you will be able to support the warehouse that you choose.

    Which warehouse makes sense for your business?

    Below are some additional comparisons to help guide you to picking the right solution.

  • Security: Redshift includes a deep bench of encryption solutions, but Snowflake provides security and compliance features oriented to which of the five options you choose.
  • Bundled features: Redshift bundles compute and storage to provide the immediate solution that can scale to an enterprise level data warehouse. Snowflake provides a business the flexibility to purchase only the features they need while giving the capability to scale later.
  • JSON: Both warehouse store JSON but Snowflake JSON support is a little bit stronger then Redshift. When you load JSON into Redshift you can use their build in functions but there are limitations where as Snowflake you can store and query JSON natively.
  • Data Lakes vs Data Warehouses

    Data Lakes versus Data Warehouses

    What is a data lake?
    A data lake is a central repository that allows you to collect and hold all of your unstructured and structure data at any scale. What that sentence means is that you do not have to transform your unstructured data to store and then run analytics on it. You can store all your data as-is.

    Do I need a data lake?
    Companies that have put data lakes in place outperformed like companies by 9% which was reported by an Aberdeen survey. What was found is that these companies were able to do new types of analytics (even create new products) from their sources of data like log files, social media, IOT, etc being stored inside of data lakes. Being able to parse and learn from this data enabled them to react faster to what their data was telling them. Looking at unstructured and structure data allowed these companies to attract and retain customers and make better informed decisions.

    Data Lakes versus Data Warehouses
    You have to approach this decision with facts and requirements. Depending on the needs to the business you might need a data warehouse, a data lake, or even both. Allow the needs of the business and what data the business collects drive the decision organically. Before we go any further lets define each of these.

    Data Warehouse is a system that pulls together data from many different sources within the business usually transactional systems that is needed by the business to conduct day to day operations. This data is collected for reporting and analysis.

    Data Lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. By allowing the data to remain in its native format, the size of the data can be greater and pulled in a more timelier manner which will give the business quicker insight what is going on.

    Lets compare Data Warehouses to Data Lakes side by side to help see what is best for your business to use for its analytic needs.

    Section Data Lake Data Warehouse
    Schema Written at the time of analysis Architected prior to streaming the data in.
    Data This is both non-relational and relational data from web sites, social media, IOT and business applications Transactional systems and operational databases
    Performance and Price Low cost storage and queries are getting faster Higher cost storage and fast queries
    Data Quality This is raw data. May or may not be curated This data is greatly curated and serves a Single Source of Truth
    Users Data scientists, business analysts, data developers Business analysts
    Analytics Predictive analytics, data discovery, and machine learning Business Intelligence and batch reporting