Build a Snowflake data lake or data warehouse

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Integrate data in real-time on your Snowflake data lake

A single repository for all your raw data is a compelling proposition. The Snowflake Data Lake can be used to store data from your disparate sources and create real-time dashboards to report on the data quickly or run analytics to uncover fresh insights.

BryteFlow makes data accessible on your Snowflake Data lake real-time, with zero coding. BryteFlow can do this in a couple of ways; by taking the data directly to the Snowflake data warehouse from the transactional sources, real-time using proprietary log-based change data capture technology and making it ready for use instantly or load the data to Amazon S3 real-time, ready to use, in Parquet, ORC or other file formats, transform the data if required and then load to the Snowflake data lake.

BryteFlow uses enterprise log based change data capture on legacy databases like Oracle, SQL Server, SAP , MySQL and more, and from applications like Salesforce and more, to move data from the sources to the Snowflake data warehouse in real-time. It maintains a replica of the source structures in Snowflake and merges the initial and delta loads automatically with SCD type 2 history if required.

The fastest way to move your data is with BryteFlow’s log-based Change Data Capture to Snowflake
Check out BryteFlow’s data integration on the Snowflake data warehouse.     Get in touch with us for a FREE Trial.
How to load terabytes of data to the Snowflake data warehouse fast

Upload data to the Snowflake Data Warehouse fast

BryteFlow meshes tightly with the Snowflake data warehouse to provide fast data integration, in real-time. Here’s what you can do with BryteFlow on your Snowflake data lake or data warehouse.
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Change Data Capture your data to your Snowflake data lake or data warehouse with history of every transaction

BryteFlow continually replicates data to Snowflake data lake in real-time, with history intact, through log based Change Data Capture. BryteFlow Ingest leverages the columnar Snowflake database by capturing only the deltas (changes in data) to Snowflake keeping data in the Snowflake database synced with data at source.

Data is ready to use – Get data to dashboard in minutes

BryteFlow Ingest on the Snowflake data warehouse provides a range of data conversions out of the box including Typecasting and GUID data type conversion to ensure that your data is ready for analytical consumption or for Machine Learning purposes.

Transfer data with speed and performance to Snowflake

BryteFlow Ingest uses fast log-based CDC to replicate your data to the Snowflake data lake. Data is transferred to the Snowflake data warehouse at high speeds in manageable chunks using compression and smart partitioning.

Automated DDL and performance tuning in the AWS-Snowflake environment

BryteFlow helps you tune performance on the AWS-Snowflake environment by automating DDL (Data Definition Language) which is a subset of SQL.

BryteFlow offers flexibility for data preparation

You have the choice of transforming and retaining data on AWS S3 and pushing it selectively to the Snowflake data warehouse – for multiple use cases including Analytics and Machine Learning. Or replicating and transforming data directly on the Snowflake data lake itself.

Make Snowflake’s performance faster by preparing data on the AWS S3 data lake

BryteFlow frees up the resources of the Snowflake data warehouse by preparing your data on Amazon S3 and only pushing the data you need for querying onto Snowflake.

Save on storage and boost Snowflake cluster performance

You can choose to save all your data on Amazon S3 where typically storage costs are much lower. On the Snowflake data warehouse you need to only pay for the resources you actually use for the compute – this can translate to a large savings on data costs. This also enhances the performance of the Snowflake cluster.

Automated Data Reconciliation on the Snowflake data warehouse

You are assured of getting high quality, reconciled data always with BryteFlow TruData, our data reconciliation tool. BryteFlow TruData continually reconciles data in your Snowflake data lake with data at source. It can automatically serve up flexible comparisons and match datasets of source and destination.

Ingest large volumes of data automatically to your Snowflake data warehouse with BryteFlow XL Ingest

If you have huge petabytes of data to replicate to your Snowflake data lake or data warehouse, BryteFlow XL Ingest can do it automatically at high speed in a few clicks. BryteFlow XL Ingest has been specially created to cater for the replication of large data sets. It uses partitioning and multi-thread loading to load bulk data fast.

Dashboard to monitor data latency and status of data ingestion on Snowflake

Stay on top of your data ingestion to the Snowflake data lake or data warehouse with the BryteFlow ControlRoom. It gives you the specifics of your Snowflake data including latency, operation start time, operation end time, volume of data ingested and data remaining.

Data transformation with data from any database, incremental files or APIs

BryteFlow Blend our data transformation tool enables you to merge and transform data from virtually any source including any database, any flat file or any API for querying on your Snowflake data lake.

Data migration from Teradata and Netezza to the Snowflake data warehouse

BryteFlow can migrate your data from data warehouses like Teradata and Netezza to your Snowflake data warehouse with ease in case you need to shift your data.

Get built-in resiliency for data integration on Snowflake

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. This is ideal for Snowflake’s big data environment which routinely handles data ingestion and preparation of thousands of petabytes of data.

Why use the Snowflake Data Warehouse?

The Snowflake data warehouse is based on SQL and is easy to use.

Data can be queried with standard SQL query language

The Snowflake database was designed as a fully functioning SQL database. The Snowflake SQL database is a columnar-stored relational database that works with Excel, Tableau and other common software. Snowflake data can be queried with standard SQL query language. Data analysts are extremely familiar with SQL and can get started on analytics fast.

The Snowflake data warehouse is a highly automated SaaS offering

Your Snowflake data warehouse or data lake will not require expensive hardware or software to be installed or configured. The custom-built Snowflake big data infrastructure is fully managed and maintained by Snowflake.

The Snowflake data lake/data warehouse is highly dynamic and scalable

You don’t need to worry about the size of your Snowflake database. Your Snowflake data lake/data warehouse is highly dynamic and scalable. Snowflake’s big data architecture is shared and multi-cluster with each cluster accessing data and running independently without conflict. This is ideal for running large queries and operations simultaneously.

Data in the Snowflake data warehouse is extremely secure

Snowflake automatically encrypts all data. Multi-factor authentication and granular access control is reassuring too. The Snowflake cloud data warehouse uses third party certification and validation to make sure security standards are met. Access control auditing is available for everything including data objects and actions within your Snowflake data lake.