What is Azure Synapse?
Azure Synapse is an analytics service that helps you bring together Big Data analytics and enterprise data warehousing. It gives the freedom to query data on your own terms, using either provisioned resources or server less on-demand. You can ingest, prepare, serve and manage data for machine learning, and immediate BI needs.
What is Google BigQuery?
It is a Google Cloud Platform to an enterprise data warehouse for analytics. It is good for analyzing the 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. With the growth of business, managing data becomes a tough task. 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.
Comparision between BigQuery and Azure Synapse
| Attributes | Google BigQuery | Azure Synapse |
|---|---|---|
| G2 Rating |  |  |
| Pricing | Query-based pricing. | No upfront costs, No termination fees. Pay only for what you use. |
| Scalability | 1-Handles everything. 2-Removes manual scaling. | 1-Easy to scale up or down. 2-Automate scalability. |
| 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. |
| 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. |
| 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. |
| Administration | It is “serverless”. Compute and storage resources are handled automatically. | Database administrators can automate query optimization. |
| 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 | Google BigQuery | Azure Synapse |
|---|---|---|
| Data Integrations | Read data using streaming mode or batch mode. | Deeply integrated Apache Spark and SQL engines. |
| Data Compression | In parallel, data is compressed before transfer while for CSV and JSON, it loads uncompressed files. | Supports row and page compression for rowstore tables and indexes, and supports columnstore and columnstore archival compression for columnstore tables and indexes. |
| Data Quality | Advanced data quality with SQL. | Better analytics at scale. |
| 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. |
| 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 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 prediction needs. |
| On-Premise | Google BigQuery | Azure Synapse |
|---|---|---|
| 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. |
| BI Tool Integration | BI is responsible for (RLS) Row Level Security and applying user permissions. | Integrated AI and BI. |
| Data lake Integration | Data like API systems use Google Cloud composer to schedule Bigquery Processing. | Files are read in the Data Lake in Parquet format, which achieves a much higher performance improving Polybase execution over 13x. |
| Cloud | Multicloud analytic solution. It is Google Cloud fully managed warehouse. | Cloud native, distributed SQL processing engine. |
| Sharing | Google BigQuery | Azure Synapse |
|---|---|---|
| Sharing | Securely access and share analytical insights in a few clicks. | Provides full visibility into your data sharing relationships. Share and receive data in any format to or from Azure Synapse analytics. |
| 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. |
| Data Security | Security model based on Google Clouds. IAM capability. Column level security. | Secure, monitor and manage your data and analytics solutions with a wide range of industry-leading security and compliance features. |
| Data Storage | Nearline storage. | Good fit for a data warehouse with a small data size and low volume data loads. |
| Backup & recovery | Automatically backed up. | Automatically backed up. |
START PLANNING YOUR MODERNIZATION
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Why Lyftrondata is your best choice?
Lyftrondata eliminates traditional ETL/ELT bottlenecks with automatic data pipelines and makes data instantly accessible to BI users. Lyftrondata supports you with 300+ data integrations such as ServiceNow, Zendesk, Shopify, Paylocity, etc. to software as a service SaaS platforms. Lyftrondata allows you to pick data from any source and migrate it to a data warehouse of your choice while allowing you to instantly analyze it in BI tools. It changes the way how data is prepared. Data from all data sources are instantly accessible in one place, a virtual data warehouse. Lyftrondata is a Data Hub for Analytics that is a central place to find all data, create shared data sets and manage data replication to a Data Warehouse.
Read More: Comparsion between BigQuery vs Redshift
How Lyftrondata helps
- Lyftrondata provides cumulative data from a different source and brings it down to the data pipeline.
- It works on the pain-points of data preparation, thus avoiding project delays.
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- It eliminates traditional bottlenecks related to data.
- It works at solving problems such as huge time consumption to generate reports, waiting to get new reports, real-time data, and data inconsistency.
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- It helps in combining other data sources to the target data Warehouse.
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Lyftrondata use cases
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Data Lake:
Lyftrondata combines the power of high-level performance and cloud data warehousing to build a modern, enterprise-ready data lake.
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Data Migration:
Lyftrondata allows you to migrate a legacy data warehouse either as a single LIFT-SHIFT-MODERNIZE operation or as a staged approach.
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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.
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Master Data Management:
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Application Acceleration:
With Lyftrondata, you can boost the performance of your application at an unprecedented speed, high security, and substantially lower costs.
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IoT:
Powerful analytics and decision making at the scale of IoT. Drive instant insights and value from all the data that IoT devices generate.
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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.
