Data integration on the S3 Data Lake
What is the AWS S3 data lake?
As everyone knows AWS S3 or Amazon’s Simple Storage Service is one of the key AWS services. It is basically a cloud data repository and can store and retrieve all kinds of online data. Users can build data lakes on AWS S3 that are infinitely scalable, fast and secure. The cherry on top is that S3 data lake storage is very inexpensive and unlike a data warehouse you do not have to consider which data takes priority for storage – you can store all of it! BryteFlow for AWS ETL
Role of the Amazon S3 bucket in the S3 data lake
The Amazon S3 bucket is a kind of container that stores the objects within your S3 database. An object is basically your data which has a key (name defined) and metadata. An object can go up to 5 TB in size and data can consist of any files / formats. When data is added to the Amazon S3 bucket in your S3 database, Amazon S3 creates a version ID that is unique and assigns it to the object. Users need to specify the Amazon S3 storage class for each object when created. Learn how to create an AWS Data Lake 10x faster
The fastest way to move your data is with BryteFlow’s log-based Change Data Capture to AWS S3
Which one do you need? Data Lake vs Data Warehouse
Check out BryteFlow’s data integration on Amazon S3. Get in touch with us for a FREE Trial.
BryteFlow’s Technical Architecture
How BryteFlow works with the Amazon S3 Data Lake
BryteFlow meshes tightly with Amazon S3 and AWS services to provide fast data integration, in real-time. Here’s what you can do with BryteFlow on your Amazon S3 data lake. Get a Free Trial of BryteFlow
Build a continually updated Raw Data Lake with history of every transaction
Build a continuously updated, Transformed Data Lake
Build a continuously updated Reconciled Data Lake
Build an S3 Data Lake at scale
If you have petabytes of data coming in, an S3 Data Lake can just scale up by adding additional EMR clusters, ingest all the data and then some more. BryteFlow’s XL Ingest helps to ingest large volumes of data without a hiccup.
Get flexible: prepare your data on the S3 data lake and push data to Redshift or Snowflake
BryteFlow lets you replicate data and prepare it on S3 that can be pushed to Redshift or Snowflake for querying. This helps reserve the resources of your data warehouse for the actual querying while the heavy hitting is done in the S3 data lake. BryteFlow for AWS ETL
In a hurry to access data? Prepare your data on S3 and use Redshift Spectrum to view data on Redshift
BryteFlow prepares data on the Amazon S3 data lake that can be viewed on Redshift through Redshift Spectrum. You don’t have to wait for data to load on Redshift – Amazon Redshift Spectrum can query your data with SQL as it resides on Amazon S3.
Migrate your data from Teradata and Netezza to Redshift and Snowflake
BryteFlow can migrate your data from data warehouses like Teradata and Netezza to Redshift and Snowflake with ease in case you’re wondering.
Automate Modern Data Architecture with BryteFlow
Modern data architecture implies low data latency, centralized data access and the capability to store data in its original format. It can scale up to handle huge volumes of data and process data in multiple formats fast. BryteFlow with AWS services on S3 provides all these things – with automation thrown in so multi-source data can be replicated, merged and prepared easily in just a few clicks – no coding required.
Data replication with Change Data Capture from any database, incremental files or APIs
BryteFlow enables you to replicate data to Amazon S3 using log-based CDC from any source including any database, any flat file or any API.
Get built-in resiliency
BryteFlow has an automatic network catch-up mode. It just resumes where it left off in case of power outages or system shutdowns when normal conditions are restored.
Why use the AWS S3 Data Lake as
your Cloud Data Repository
Amazon S3 storage has a number of built-in advantages:
- An Amazon S3 Data Lake provides infinite scalability. Your data can grow without worries.
- Data on in the S3 data lake does not need to be transformed, it can be stored in raw format and can be queried with AWS services like Amazon Athena.
- User authentication is possible so your data in the Amazon S3 bucket is not subjected to unauthorized access.
- Bucket policies can be defined by the bucket owner for centralized access control for objects and Amazon S3 buckets.
- AWS Identity and Access Management (IAM) can manage access to data on the S3 database.
- Many versions of the same object can be stored in the same Amazon S3 bucket with versioning.