Cloud data warehouses: The future of data management
What is BigQuery?
It is a Google Cloud Platform to an enterprise data warehouse for analytics. It is good for analyzing a huge amount of data to meet big data processing requirements. The provided data is encrypted, durable, and highly available. It offers Exabyte-scale storage and petabyte-scale SQL queries. Managing ever-growing data for enterprises is tough. This focus can be reshifted to analyze business-critical data. Dremel is a powerful query engine developed by Google that is used to execute queries in BigQuery.
What is Redshift?
Redshift can be described as a fully-managed, cloud-ready petabyte-scale data warehouse service that can be seamlessly integrated with business intelligence tools. Extract, transform, and load has to be done to make business smarter. To launch a cloud data warehouse, a set of nodes has to be launched called the Redshift cluster. Regardless of the size of data, one can take advantage of fast query performance.
What is Snowflake?
Snowflake is a powerful relational database management system. It’s an analytical data warehouse for both structured and semi-structured data that follows the SaaS model. It is fast, user-friendly, and offers more flexibility than traditional Warehouses. It uses an SQL database engine with a unique architecture specifically designed for clouds.
Comparision between Amazon Redshift, BigQuery and Snowflake
| Attributes | BigQuery | Redshift | Snowflake |
|---|---|---|---|
|
G2 Rating |
|
|
|
|
Maintenance |
More time in optimizing queries. Partitions and sorts data in a background. |
Some operations need to be manual. Query planner still relies on table statistics for updating the stats. |
Little to maintain. It supports automatic clustering. It has depressed manual clustering. |
|
Data Sources |
One can set up a connection to some external data storage like Cloud SQL |
We can connect to data sitting on S3.
Act as an intermediate compute layer. |
The data has to be stored within Snowflake Provides extra table functionality. |
|
Streaming |
Native streaming |
No native streaming |
No native streaming. Microbatching via Snowpipe from data sitting on Google Cloud. |
|
Caching |
Caches queries and has an adjustable intermediate cache. |
Caches queries and results depending on the node type. |
Hot and warm query caches in intermediate storage. |
|
Materialise
View |
Currently in GA |
Has good support for materialized view |
Full support for the materialized view. |
|
UDF's |
Supports writing UDF's in SQL and Javascript. |
UDFs can be written in SQL and PYTHON |
Support for functions in SQL and Javascript. |
|
Query Language |
Offers two main dialect
Legacy SQL Standard SQL
|
The SQL syntax is also an ANSI complaint. |
ANSI complaint. Simple to use.
|
|
Encryption |
Encrypted using Google-managed encryption key management system(KMS) |
End-to-End encrypted by default |
Encrypted at rest using the AWS management system. |
|
Scalability |
Only charged for queries you run |
Recently induced RA3 node offers both elastic resize or classic resize |
Pause, resume semantics both manual and automated based on workload. |
|
Pricing |
2 cents per GB for warm data, 1 cent per GB for colder data |
Node type (ds2/dc2/RA3, avoid d*1 node type. The Use of Redshift spectrum may incur additional charges. |
With the number and size of warehouses, you will get cost per credit. |
|
Cloud Deployment |
Multicloud analytic solution. It is Google Cloud fully managed ware house. |
Fully managed petabyte scale data ware house service in Cloud. |
With it’s Cloud data platform live data can be shared. |
|
Compression |
Proprietary compression that is opaque to the user |
Transparent compression by implementing open algorithms |
It provides its compression layer that is opaque to the user. |
| Core Competencies | BigQuery | Redshift | Snowflake |
|---|---|---|---|
|
Data Integrations |
Read data using streaming mode or batch mode. |
Advanced ETL tool helps you effortlessly by collecting data. |
ETL/ELT concept in data integration. |
|
Data Compression |
In parallel, data is compressed before transfer while for CSV and JSON, it loads uncompressed files. |
Columnar compression |
Gzip compression efficiency. |
|
Data Quality |
Advanced data quality with SQL. |
Python data quality for amazon shift. |
With tools like Talend provide data management with real time speed. |
|
Built-In Data Analytics |
Fully manages enterprise data for large scale data analytics. |
Know is a BI tool used for Amazon Redshift. |
A single platform that creates cloud. |
|
In-Database Machine Learning |
Bigquery ML let you create and execute machine. learning models using SQL queries. |
Create data source wizard is used in Amazon Machine Learning to create data source object. |
SQL dialect like
‘Intelligent Miner’ and ‘Oracle’ is being used. |
|
Data Lake Analytics |
Uses Identity and Access Management (IAM) manage access to resources to analyse data. |
Uses amazon S3. It is cost efficient and stores unlimited data. |
Global snowflake turns data lake into data ocean. |
| Integration | BigQuery | Redshift | Snowflake |
|---|---|---|---|
|
AI/ ML Integration |
Use bigquery ML to evaluate ML models.
|
Create data source wizard in (Amazon ML).
|
Driveless A1
Automated machine learning inflows. |
|
BI Tool Integration |
BI is responsible for (RLS) Row Level Security and applying user permissions. |
Know is a BI tool used in Redshift. |
Built -for -cloud warehouse deliver efficient BI solution. |
|
Data lake Integration |
Data like API system use Google Cloud composer to schedule Bigquery Processing. |
Integrated with data lake to offer 3x performance. |
It is a modern data lake. |
| Sharing | BigQuery | Redshift | Snowflake |
|---|---|---|---|
|
Sharing |
Securely access and share analytical insights in few clicks.
|
Share data in Apache Parquet Format.
|
Enables sharing through shares between read-only. |
|
Data Governance |
Using google cloud that allows customers to abide by GDPR , CCTA and over regulations. |
Data Lienage using Token. |
Data governance experts like Talend provides perfect data governace. |
|
Data Security |
Security model based on Google Clouds. IAM capability.Column level security. |
Network isolation to control access to data warehouse cluster. SSL and AES 256 encryption end – to – end encryption. |
Role Based Access Control (RBAC) authorization. |
|
Data Storage |
Nearline storage |
Columnar storage |
Uses new SQL database. |
|
Backup & recovery |
Automatically backed up. |
Automatically backed up. |
Does with virtual warehouse and querying from clone. |
START PLANNING YOUR MODERNIZATION
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How Lyftrondata helps
- Lyftrondata provides cumulative data from a different source and brings it down to the data pipeline.
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- It helps in combining other data sources to the target data warehouse.
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Lyftrondata use cases
-
Data Lake:
Lyftrondata combines the power of high-level performance and cloud data warehousing to build a modern, enterprise-ready data lake.
-
Data Migration:
Lyftrondata allows you to migrate a legacy data warehouse either as a single LIFT-SHIFT-MODERNIZE operation or as a staged approach.
-
BI Acceleration:
Limitlessly scale your BI. Query any amount of data from any source and drive valuable insights for critical decision making and business growth.
-
Master Data Management:
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-
Application Acceleration:
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-
IoT:
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Data Governance:
With Lyftrondata, you get a well-versed data governance framework to gain full control of your data, better data availability and enhanced security.
Lyftrondata delivers a data management platform that combines a modern data pipeline with agility for rapid data preparation. Lyftrondata supports you with 300+ data integrations such as ServiceNow, Zendesk, Shopify, Paylocity, etc. to software as a service SaaS platforms. Lyftrondata connectors automatically convert any source data into the normalized, ready-to-query relational format and provide search capability on your enterprise data catalog. It eliminates traditional ETL/ELT bottlenecks with automatic data pipelines and makes data instantly accessible to BI users with the modern cloud compute of Spark & Snowflake.
It helps easily migrate data from any source to cloud data warehouses. If you have ever experienced a lack of needed data, your report generation is too time consuming, and the queue for your BI expert is too long, then consider Lyftrondata.
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