BigQuery vs Azure Synapse

What is Azure Synapse?

An analytics tool called Azure Synapse makes it easier to combine business data warehousing and big data analytics. It offers you the flexibility to query data as you see fit, leveraging server-less on-demand or supplied resources. For urgent BI needs and machine learning, you may ingest, prepare, serve, and manage data.

Comparision between BigQuery and Azure Synapse

Highlights

#	Features	Google BigQuery	Azure Synapse
1	G2 Rating
2	Pricing	Query-based pricing.	No upfront costs, No termination fees. Pay only for what you use.
3	Scalability	1-Handles everything. 2-Removes manual scaling.	1-Easy to scale up or down. 2-Automate scalability.
3	Performance	Ability to autoscale. Perform well under load levels.	Averaging the fastest execution time at 2,996 seconds for the entire workload of 103 field test queries.
4	Security	Use AES encryption. Federated user access via Microsoft Active Dictionary. MFA.	Transparent Data Encryption (TDE) helps protect against the threat of malicious activity by encrypting and decrypting your data at rest.
5	Architecture	Takes into account computation and storage.	Synapse SQL leverages a scale-out architecture in order to distribute computational processing of data across multiple nodes.
6	Administration	It is “serverless”. Compute and storage resources are handled automatically.	Database administrators can automate query optimization.
7	Data Protection	Protects through Google Cloud Platform's Virtual Private Cloud Service Controls.	Features are built into the fabric of Azure Synapse, like automated threat detection & always-on data encryption.

Core Competencies

#	Features	Google BigQuery	Azure Synapse
1	Data Integration	Read data using streaming mode or batch mode.	Deeply integrated Apache spark and SQL engine.
2	Data Compression	In parallel, data is compressed before transfer while for CSV and JSON, it loads uncompressed files.	Support page and row compression for row tables and indexes and support column store and column store archival compression for couumnstore table and indexes.
3	Data Quality	Advanced data quality with SQL.	Better analytics at scale.
4	Built-In Data Analytics	Fully manages enterprise data for large scale data analytics.	Limitless analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless or provisioned resources—at scale.
5	In Database Machine Learning	Bigquery ML lets you create and execute machine learning models using SQL queries.	Build a predictive machine learning model based on data stored in Azure Synapse. Data acquisition and understanding.
6	Data Lake Analytics	Uses Identity and Access Management (IAM) to manage access to resources and analyze data.	Single service for all workloads when processing, managing and serving data for immediate business intelligence and data predication needs.

Integration

#	Features	Google BigQuery	Azure Synapse
1	AI/ML Integration	Use bigquery ML to evaluate ML models.	Complete your end to end analytics solution with deep integration of Azure Machine Learning and Power BI.
2	BI Tool Integration	BI is responsible for (RLS) Row Level Security and applying user permissions.	Integrated AI/BI.
3	Data Lake Integration	Data like API systems use Google Cloud composer to schedule Bigquery Processing.	Files are read in Data Lake in Parquet format, which achieves a much higher performance improving Polybase execution over 13x.
4	Cloud	Multicloud analytic solution. It is Google Cloud fully managed warehouse.	Cloud-native, distributed SQL processing engine.

Sharing

#	Features	Google BigQuery	Azure Synapse
1	Sharing	Securely access and share analytical insights in a few clicks.	Provides full visibility in your data sharing relationships. Share and receive data in any format to or from Azure Synapse Analytics.
2	Data Security	Security model based on Google Clouds. IAM capability. Column level security.	Secure, Monitor and Manage your data and analytics solution with a wide range of industry-leading security and compliance features.
3	Data Governance	Using google cloud that allows customers to abide by GDPR , CCTA and over regulations.	Offers cloud governance capabilities to keep your company compliant with regulations and help your developers deliver software faster.
4	Data Storage	Nearline storage.	Good fit for a data warehouse with a small data size and low volume data loads.
5	Backup and Recovery	Automatically backed up.	Automatically backed up.

Why Lyftrondata is your best choice?

With autonomous data pipelines, Lyftrondata removes typical ETL/ELT bottlenecks and gives BI users fast access to data. With over 300 data connectors to software as a service (SaaS) platforms, like ServiceNow, Zendesk, Shopify, Paylocity, and others, Lyftrondata can help you. With Lyftrondata, you can select data from any source, move it to the data warehouse of your choosing, and use BI tools to analyze it right away. It modifies the process of preparing data. Instantaneous access to data from any source is possible in one location—a virtual data warehouse. A central location to locate all data, build shared data sets, and oversee data replication to a data warehouse is Lyftrondata, an analytics data hub.

How Lyftrondata helps

• Lyftrondata provides cumulative data from a different source and brings down to the data pipeline.
• It works on the pain-points of preparation of data avoiding delays in projects.
• It also converts the complex data into the normalized one.
• It eliminates traditional bottlenecks related to data.
• It works at solving problems like huge time consumption to generate reports, waiting to get new reports, real-time data, and data inconsistency.
• It democratizes data management.
• It helps in combining other data sources to the target data Warehouse.
• It perfectly integrates the data and enables data masking and encryption to handle sensitive data.
• It provides a data management platform for rapid data preparation with agility, combining it with the modern data pipeline.
• It empowers business users to solve data-driven business problems.
• It reduces the workload of prototyping tools while optimizing offload data.

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:

Scale your BI limitlessly. Query any amount of data from any source and drive valuable insights for critical decision making and business growth.

Master Data Management:

Lyftrondata enables you to work with chosen web service platforms and manage large data volumes at an unprecedented low cost and effort.

Application Acceleration:

With Lyftrondata you can boost the performance of your application at an unprecedented speed, high security, and substantially lower costs.

IoT:

Powerful analytics and decision making at the scale of IoT. Drive instant insights and value from all the data that IoT devices generate.

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.