In the Snowflake as a Data Lake blog, we saw the importance of the data lake and its technical challenges and how Snowflake can act as a data lake solution. We also touched on a few points on how a data lake can be implemented in Snowflake. In this part of the blog, we will see how Snowflake outplays other competitors in the market, like Amazon S3 and Delta Lake. Show
We are considering the following factors for comparison:
Snowflake has faster analytics, simple service, stores diverse data across various cloud platforms, and can be scaled up as required; this makes it one of the most cost-effective solutions in the market. Snowflake has a single integrated service across the three major clouds. You can have data stored in Microsoft Azure, Amazon S3, or Google Cloud but can still integrate all of them inside Snowflake. In the future, if we want to move data across cloud vendors, Snowflake would still be able to work efficiently. Built entirely on ANSI SQL, it is effortless for one to have a data lake that has a full SQL environment. Complete resource isolation and control enables Snowflake virtual warehouses to independently fetch queries from the same object without one affecting the other. Automatic metadata management and history allow Snowflake to produce faster analytics with built-in control and governance for fast data flow. Hence with Snowflake, we can extract batch or streaming data and build materialized views, external tables and then deliver the insights and business results much faster. Most importantly, it does not require manual intervention to rescale the cluster. As compute cost and storage cost are separated, it keeps the cost low, thus making it to be the top contender for data lakes in the market. Learn more about Visual BI Solutions Snowflake offerings here and read more about similar Self Service BI topics here. An S3 Data Lake offers an elastic, highly scalable, cost-effective data lake solution for enterprises. Basically, S3 is an object store, it is a managed service offered by AWS and is an acronym for Amazon Simple Storage Service (S3). An S3 data lake can store any kind of data – structured or unstructured – and can be used to ingest any data and make it available for centralized access across an enterprise. An S3 data lake is extremely secure, and data is protected with 99.999999999% (11 9s) of durability. Get Automated Upserts on S3 without Apache Hudi Quick LinksWhy choose Amazon S3 for Data Lake Implementation?Whether you need data lake analytics or a data lake for storage, there are so many reasons why Amazon S3 is one of the top choices for cloud data lake implementation. Here we provide you some great reasons to have S3 as a data lake and a video series to guide you in creating your own S3 data lake in minutes. Build a Data Lakehouse on S3 without Hudi or Delta Lake Amazon S3 integrates tightly with native AWS ServicesAn S3 Data Lake can integrate with native AWS services to enable critical activities like high-performance computing (HPC), big data analytics, artificial intelligence (AI), machine learning (ML). For example, Amazon S3 integrates with Amazon Redshift for data warehousing, with Amazon Athena for adhoc analysis, Amazon SageMaker for Machine Learning, AWS Lambda for serverless compute and Amazon Kinesis for data streaming, just to name a few. AWS DMS Limitations for Oracle Replication An S3 Data Lake lets you separate storage and compute, leading to lower costsAn S3 data lake effectively allows the separation of storage and compute. Unlike traditional data warehousing solutions where compute and storage are coupled and costs are high, on Amazon S3 you can store huge amounts of data in its native format quite economically. You can spin up virtual servers (only what you need for the compute) using Amazon Elastic Compute Cloud (EC2) or Amazon Elastic Map Reduce (EMR). So, in effect you only pay for the compute when you need it. Need a Data Lake or a Data Warehouse? Amazon 3 Security, Access Management and Compliance and EncryptionAmazon S3 security is comprehensive. Your S3 data lake will have advanced security and encryption features, making it a very versatile and secure data lake solution. It also has access management tools and compliance programs to aid in meeting regulatory requirements. AWS Identity and Access Management (IAM) Policy and Permissions AWS Identity and Access Management (IAM) manages user creation and access management. The IAM policy you create, defines Read and Write access to objects in a specific S3 bucket. Access Control Lists (ACLs) control accessibility of individual objects, bucket policies exist for configuring permissions for individual objects within an S3 bucket. S3 also has audit logs to display requests made for accessing data. S3 Encryption for a secure S3 Data Lake S3 Encryption is about protection of data while data is in transit to and from Amazon S3 and while it is at rest, stored in Amazon S3 data centers. In transit, data can be protected by using Secure Socket Layer/Transport Layer Security (SSL/TLS) or client-side encryption. Server-Side Encryption: Amazon S3 is requested to encrypt the object before saving it on disks and decrypting it on download. Client-Side Encryption: Data can be encrypted client-side and then uploaded to your S3 data lake. Here the encryption is managed by you -the encryption process, encryption keys and other tools. An S3 Data Lake provides centralized access to data and removes data silosAn S3 data lake acts as a centralized data store and does away with data silos allowing users to access data securely for analytics and machine learning. users can analyze common datasets with their individual analytics tools and avoid distribution of multiple data copies across various processing platforms, leading to lower costs and better data governance. Learn how to build an AWS Data Lake 10x faster. Issues with S3 Data IngestionData ingestion to S3 can be tricky when only changed data is delivered to the data lake for performance reasons. Delivering full data sets in some cases is just not possible or can put a heavy load on the source system. Unlike a data warehouse, where changed data or deltas can be handled easily using an ‘upsert’ operation (update if the primary key exists, else insert the record), on an S3 data lake it is a bit more challenging to update data with the deltas. This is because Amazon S3 is an object store and the process requires engineering effort and integration with third party software like Apache Hudi. Learn about CDC to S3 Build an S3 Data Lake with BryteFlowAn S3 Data Lake with BryteFlow neatly sidesteps issues you may face in a typical S3 data ingestion. BryteFlow delivers near real-time data or changed data in batches as configured, using log-based CDC from databases like SAP, Oracle, SQL Server, MySQL, Postgres etc. Change Data Capture Types and CDC Automation BryteFlow provides automated upserts on the S3 Data Lake In order to sync data with changes at source, BryteFlow does an automated upsert on Amazon S3 without coding or any integration with Apache Hudi. It delivers an end-to-end solution from the source to the S3 data lake with every best practice included – S3 security including KMS, S3 partitioning, Amazon Athena and Glue Data Catalog integration, and configuration of file types and compression e.g. Parquet -snappy. Learn about BryteFlow for AWS ETL BryteFlow provides time -series data on your S3 Data Lake BryteFlow can also create a time-series / SCD type 2 data lake on S3 if configured. BryteFlow XL Ingest allows you to bulk load data to S3 fast and easily with multi-threaded parallel loading, smart partitioning and compression. With fast time to value, enterprises can scale effortlessly in their data integration projects, enabling valuable data engineering resources to spend more time analyzing the data rather than ingesting it. Compare AWS DMS with BryteFlow for replication to AWS Cloud. Build an S3 Data Lake in Minutes with BryteFlow – Amazon S3 Tutorial (4 Part Video)The following Amazon S3 Tutorial video series demonstrates how you can create an S3 Data Lake without any coding and in real-time with BryteFlow. It describes how you can bring your data from a SQL Server database in near real-time to S3 and build an S3 data lake in just one day. Get a Free Trial of BryteFlow Video 1: Connect your Source Database and Destination Database on Amazon S3: Video 2: How to provide Additional Permissions, create Roles and Policies, and fill in AWS Cloud Credentials on S3: Why use S3 as data lake?Amazon S3 is the best place to build data lakes because of its unmatched durability, availability, scalability, security, compliance, and audit capabilities. With AWS Lake Formation, you can build secure data lakes in days instead of months.
What is difference between S3 and bucket?Amazon S3 is an object storage service that stores data as objects within buckets. An object is a file and any metadata that describes the file. A bucket is a container for objects. To store your data in Amazon S3, you first create a bucket and specify a bucket name and AWS Region.
How many data lakes does Amazon S3 host?Amazon S3 hosts more than 10,000 data lakes and we wanted to showcase some recent case studies featuring customers of various industries and use cases that have built a data lake on Amazon S3 to gain value from their data.
Is S3 bucket a data warehouse?Data lakes often coexist with data warehouses, where data warehouses are often built on top of data lakes. In terms of AWS, the most common implementation of this is using S3 as the data lake and Redshift as the data warehouse.
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