It is no secret that data is precious, and the more you have, the better. Data is fantastic, but Big Data is even better. With big data, you get a broader scope of research, which ultimately goes a great way in informed decision-making. However, getting your hands on large datasets is not as easy as it seems. With extensive data, comes several complexities that make a Data Scientist’s job even more complicated. A myriad of factors come into play, making it extremely difficult for you to access such information.
The future with Big Data may sound bleak, but that is not the case with Google’s BigQuery and Bigtable. With these tools, you can easily explore the world of open data. In this article, you’ll be taken through critical aspects of Bigtable vs BigQuery. But beforehand, let’s discuss the two platforms in brief.
Table of Contents
Introduction to BigQuery
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Google’s BigQuery is a powerful Business Intelligence (BI) platform that works as a “Big Data as a Service” solution. Google BigQuery is a tool that creates real-time analytic reports of Big Data to help you generate useful insights to make effective business decisions. BigQuery is a fully managed, serverless SQL Data Warehouse that facilitates speedy SQL queries and interactive analysis of large datasets (in the order of Terabytes or Petabytes). Other Data Warehouse solutions from major public Cloud providers, such as Amazon Web Services’ Redshift or Microsoft’s Azure SQL Data Warehouse, are in contention with Google’s BigQuery.
The BigQuery platform uses a Columnar Storage paradigm to speed up data scanning and a “Tree Architectural Model” to make searching and aggregating results much easier and more efficient. BigQuery’s fast data processing, according to Google, can scan 35 Billion rows in a matter of seconds. BigQuery can read and write data directly from Hadoop/Spark and Beam applications, and it can be linked into the Apache Big Data ecosystem.
It is important to note that Google BigQuery works best with single tables. So, if the data you are trying to input is in multiple tables, it’s best to merge them into a single table before entering the query.
Introduction to Bigtable
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Google’s Bigtable, often known as a “NoSQL Database as a Service” is a Petabyte-scale, fully managed NoSQL database service. Bigtable can index, query, and analyze enormous volumes of data and supports weak consistency.
Bigtable is especially well-suited to store massive amounts of single-keyed data with minimal latency, with an excellent read and write speed. As a result, it’s an excellent MapReduce Data Source. Bigtable has been utilized for day-to-day operations by Google products like Analytics, Finance, Personalized Search, Earth, Orkut, and Writely (a precursor to Google Docs), servicing millions of Google users.
Now that you’ve some basic understanding of both platforms, let’s move on to Bigtable vs BigQuery.
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Google’s Bigtable vs BigQuery: A Comprehensive Comparison
Before getting into the details, let’s have a look at some of the standard terminologies essential for the Bigtable vs BigQuery comparison.
OLTP vs OLAP
When evaluating database systems, one of the most significant distinctions to make is between OLTP and OLAP systems.
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- OLTP (Online Transactional Processing): OLTP systems deal with transactional data, handle both read and write operations, and are designed to speed up the processes. This makes them ideal for keeping track of day-to-day business activities. OLTP databases can typically hold data up to 10 Gigabytes in size.
- OLAP (Online Analytical Processing): OLAP systems deal with aggregated historical data, support only read operations, and are designed to swiftly return responses to user queries. OLAP systems can hold many times the amount of data when compared to OLTP systems. OLAP systems have the capacity to hold Petabytes of data. Business Intelligence and Data Analytics are the most common applications of OLAP systems.
SQL vs NoSQL
Beyond OLTP/OLAP, probably the most important dividing factor in the database world is SQL vs NoSQL.
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- A “Relational Schema” is used in SQL Databases, which organizes data into tables, rows, and columns. SQL (Structured Query Language) is used to define and manipulate data in SQL Databases. Vertical scalability is emphasized in SQL Databases, which means that you can raise the load on a single server by increasing resources like CPU, Memory, or Disc Space.
- NoSQL Databases operate with both structured and unstructured data and use a “Non-Relational Schema” (either document, graphs, key-value, or columnar). Horizontal scalability is emphasized in NoSQL Databases, which means you can raise the load by sharding or installing several servers.
Bigtable vs BigQuery
Now that you have a basic understanding of the standard terminologies, let’s dive straight into the comparison of Bigtable vs BigQuery.
| BigQuery | Bigtable |
| BigQuery is an OLAP (Online Analytical Processing) solution. As query latency is high, BigQuery is best suited for queries with high workloads, such as standard OLAP reporting and archiving activities. BigQuery’s architecture discourages OLTP-style queries. To put this in perspective, small read-write operations in BigQuery take about 1.8 seconds, while the identical action in Bigtable takes only 9 Milliseconds. | Bigtable is ideal for OLTP workloads because of its quick read-by-key and update operations. Tables store information in the data model, and rows have columns (Type Array or Struct). A column can be added to a row; the structure is similar to that of a persistent map. Rows feature the main key that is unique to each entry, facilitating simple reading and updating.
As Bigtable has efficient support for key-range-iteration, it is possible to run reporting/OLAP workloads in it. Use BigQuery alternatively if interactive querying in an Online Analytical Processing environment is a top priority. |
| BigQuery allows you to conduct complicated analytical SQL-Based queries against big datasets. Users can, however, make use of NoSQL approaches. For performance reasons, it is recommended to denormalize data while developing schemas and importing data into BigQuery. | Bigtable is a NoSQL Database service that does not allow SQL or multi-row transactions. It is not a Relational Database. Because of this, Bigtable is inappropriate for a wide range of applications; it is only suitable for changeable data sets with a minimum data size of 1 Terabyte—anything smaller results in excessive overhead. |
| BigQuery is an excellent solution for queries that require a “Table Scan” or a search throughout the entire database (e.g. calculating Sums, Averages, Counts, or Groupings). BigQuery, on the other hand, also has a lot of limits, including a daily limit on table updates and a limit on data size per request. This is because BigQuery isn’t meant to be a replacement for typical Relational Databases; instead, it’s designed to conduct analytical queries rather than basic CRUD activities. | Bigtable‘s main features are its ability to grow horizontally (resulting in extremely high read/write throughput) and its “key-columns”—i.e., one key can have numerous columns that can be modified. When storing individual data elements larger than 10 Gigabytes, performance diminishes. Cloud storage is most likely a better alternative if you need a complete storage solution for unstructured things (e.g. video files). Bigtable works best with Time-Series data (for example, CPU and Memory utilization over time for several servers), Financial data (for example, Transaction Histories, Stock Prices, and Currency Exchange Rates), and IoT (Internet of Things) use cases. |
| BigQuery data is immutable: once uploaded, an object cannot be changed during its storage lifespan, and data cannot be deleted or altered for a set period of time. If an existing record needs to be modified, the partition must be rewritten. BigQuery is an “append-only” database that reduces the amount of data saved by automatically removing partitions that are older than the pre-configured time to live. | Bigtable organizes data into scalable tables, each of which is a sorted key/value map with a column key, row key, and timestamp as indexes. This allows for changeable data as well as quick key-based lookups. Each column has separate values for each row, and each row normally defines a single object. Regardless of how many columns are read or written within a row, read and write operations on data to rows are atomic. |
Bigtable vs BigQueryThis Bigtable vs BigQuery comparison comes in handy for organizations deciding to opt for either of them.
Conclusion
To summarise, here’s the highlight of Bigtable vs BigQuery:
- Bigtable is a mutable data NoSQL Database service that is best suited for OLTP use cases.
- BigQuery is an immutable SQL Data Warehouse that is suitable for OLAP applications like Business Intelligence and Data Analytics.
This article introduced you to Google’s BigQuery and Bigtable. Important aspects of Bigtable vs BigQuery are discussed in the later section. In case you want to export data into your desired BigQuery Data Warehouse, then Hevo Data is the right choice for you!
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In his role as a freelance writer, Muhammad loves to use his analytical mindset and a problem-solving ability to help businesses solve problems by offering extensively researched content.