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Google BigQuery vs Hadoop: What are the differences?

Introduction

Google BigQuery and Hadoop are both powerful tools used for big data processing and analysis. While they share some similarities, there are key differences between the two that make them suitable for different use cases.

  1. Scalability and Ease of Use: Google BigQuery is a fully managed service that handles the infrastructure and scalability aspects, allowing users to focus on data analysis. In contrast, Hadoop requires setting up and managing a cluster, which can be complex and time-consuming.

  2. Storage and Processing Model: BigQuery is a columnar storage system that excels in executing SQL queries on large datasets. It performs best with analytical workloads that involve aggregating and filtering large amounts of data. On the other hand, Hadoop uses a distributed file system (HDFS) and a batch processing model (MapReduce) that is well-suited for processing large volumes of raw data using custom code.

  3. On-demand Pricing vs Fixed Cluster Cost: BigQuery charges users based on the amount of data processed, making it a cost-effective solution for sporadic or unpredictable workloads. With Hadoop, you typically pay for a fixed-size cluster regardless of how much data is processed, which may be more suitable for continuous and heavy workloads.

  4. Real-time vs Batch Processing: BigQuery is designed for interactive queries and provides near real-time results, making it ideal for business intelligence and ad-hoc analysis. In contrast, Hadoop processes data in batch, making it better suited for offline data processing and long-running jobs that don't require immediate results.

  5. Ecosystem and Tooling: Hadoop has a well-established ecosystem with a variety of tools (e.g., Hive, Pig, Spark) and libraries that offer flexibility and extensibility. On the other hand, BigQuery has a narrower ecosystem but integrates well with other Google Cloud services, enabling seamless integration with other analytics and machine learning tools.

  6. Resilience and Fault Tolerance: Hadoop's distributed nature and replication strategy make it highly fault-tolerant. If a node fails, data can still be retrieved from other replicas. In contrast, BigQuery handles replication and resilience behind the scenes, ensuring data durability and availability without user intervention.

In summary, Google BigQuery is a fully managed, scalable, and cost-effective solution optimized for analyzing large datasets with SQL-like queries, while Hadoop offers more flexibility, extensibility, and batch processing capabilities for raw data processing with custom code.

Advice on Google BigQuery and Hadoop
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I have a lot of data that's currently sitting in a MariaDB database, a lot of tables that weigh 200gb with indexes. Most of the large tables have a date column which is always filtered, but there are usually 4-6 additional columns that are filtered and used for statistics. I'm trying to figure out the best tool for storing and analyzing large amounts of data. Preferably self-hosted or a cheap solution. The current problem I'm running into is speed. Even with pretty good indexes, if I'm trying to load a large dataset, it's pretty slow.

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DruidDruid

Druid Could be an amazing solution for your use case, My understanding, and the assumption is you are looking to export your data from MariaDB for Analytical workload. It can be used for time series database as well as a data warehouse and can be scaled horizontally once your data increases. It's pretty easy to set up on any environment (Cloud, Kubernetes, or Self-hosted nix system). Some important features which make it a perfect solution for your use case. 1. It can do streaming ingestion (Kafka, Kinesis) as well as batch ingestion (Files from Local & Cloud Storage or Databases like MySQL, Postgres). In your case MariaDB (which has the same drivers to MySQL) 2. Columnar Database, So you can query just the fields which are required, and that runs your query faster automatically. 3. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. 4. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures 5. Gives ana amazing centralized UI to manage data sources, query, tasks.

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Decisions about Google BigQuery and Hadoop
Julien Lafont

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

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Pros of Google BigQuery
Pros of Hadoop
  • 28
    High Performance
  • 25
    Easy to use
  • 22
    Fully managed service
  • 19
    Cheap Pricing
  • 16
    Process hundreds of GB in seconds
  • 12
    Big Data
  • 11
    Full table scans in seconds, no indexes needed
  • 8
    Always on, no per-hour costs
  • 6
    Good combination with fluentd
  • 4
    Machine learning
  • 1
    Easy to manage
  • 0
    Easy to learn
  • 39
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Amazon aws
  • 1
    Java syntax

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Cons of Google BigQuery
Cons of Hadoop
  • 1
    You can't unit test changes in BQ data
    Be the first to leave a con

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    - No public GitHub repository available -

    What is Google BigQuery?

    Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

    What is Hadoop?

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

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    What are some alternatives to Google BigQuery and Hadoop?
    Google Cloud Bigtable
    Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
    Amazon Redshift
    It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
    Snowflake
    Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
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