Need advice about which tool to choose?Ask the StackShare community!
Amazon EMR vs Google BigQuery: What are the differences?
Introduction
Amazon EMR and Google BigQuery are two popular cloud-based data analytics and processing services. While both platforms offer similar functionalities, there are key differences that set them apart. This article will highlight the key differences between Amazon EMR and Google BigQuery.
Data Processing Model: Amazon EMR provides a fully managed Apache Hadoop and Apache Spark platform, allowing users to run large-scale data processing and analytics workloads. It provides flexibility in choosing the processing framework and offers support for various data processing tools and libraries. On the other hand, Google BigQuery is a serverless data warehouse and analytics platform that focuses on executing SQL queries on structured data. It is optimized for fast query processing and can handle large data volumes efficiently.
Pricing Structure: Amazon EMR follows a pay-as-you-go pricing model based on the resources used, such as compute instances, storage, and data transfer. Users have control over the instance types and can choose to use spot instances for cost optimization. In contrast, Google BigQuery applies a pricing model based on the amount of data processed by queries and the storage used. Storage costs are calculated separately, and querying data incurs additional costs. However, it offers a free tier for small-scale usage.
Data Storage: Amazon EMR allows users to store and process data in various storage options, including Amazon S3, Hadoop Distributed File System (HDFS), and other compatible file systems. It provides flexibility in choosing the storage backend and supporting a wide range of data formats. On the other hand, Google BigQuery has its own storage system and uses a columnar storage format optimized for query performance. It automatically handles data replication, durability, and backups.
Data Import and Export: Amazon EMR provides seamless integration with other Amazon Web Services (AWS) services, making it easy to import and export data from various sources. It supports direct integration with Amazon S3, Amazon DynamoDB, and other AWS services, as well as external databases through JDBC or ODBC connectors. Google BigQuery also supports importing data from various sources, including Google Cloud Storage, Google Drive, and external databases. It offers connectors for popular data ingestion tools and supports exporting query results to various file formats.
Query Execution: Amazon EMR supports processing both batch and real-time data with tools like Apache Spark and Apache Flink. It provides fine-grained control over query execution and data transformations. Google BigQuery, on the other hand, focuses on executing SQL queries on structured data with high performance. It optimizes query execution by automatically parallelizing queries and caching intermediate results.
Ecosystem Integration: Amazon EMR integrates well with the entire AWS ecosystem, allowing users to leverage other AWS services for data processing, storage, security, and monitoring. It seamlessly integrates with services like AWS Glue for data cataloging and AWS Lambda for serverless compute. Google BigQuery integrates with other Google Cloud Platform (GCP) services, including Google Cloud Storage, Google Cloud Dataflow, and Google Cloud Pub/Sub. It also provides connectors for popular data integration and visualization tools.
In summary, the key differences between Amazon EMR and Google BigQuery lie in their data processing models, pricing structures, data storage options, data import and export capabilities, query execution strategies, and ecosystem integrations.
Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.
Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.
BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.
BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.
Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.
BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.
We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution