ScyllaDB’s Post

DYK that #DBeaver can be a simple alternative to cqlsh? Here's how you can use it to access your #ScyllaDB clusters through a GUI with syntax highlighting. https://ow.ly/9og850RBYF8 #NoSQL #SQL #database #DBeaverEnterprise

DBeaver is a universal database manager that is very helpful because you can toggle between various databases in your tech stack, view and evaluate multiple schemas, and visualize your data. Attila Tóth, Developer Advocate, shows how to easily connect #DBeaver and #ScyllaDB, as well as how to run some basic queries. https://lnkd.in/eH2qck3D #NoSQL #SQL #database #DBeaverEnterprise

Curious how to #tune #sql #queries? How to make them #faster? See our new #blog #post about Metis and learn all the best practices. https://buff.ly/4585opc #cicd #observability #monitoring #database

TimescaleDB: TimescaleDB is an open-source database designed to make SQL scalable for time-series data. It is engineered up from PostgreSQL and packaged as a PostgreSQL extension, providing automatic partitioning across time and space (partitioning key), as well as full SQL support. Hypertables : Hypertables are designed specifically for time-series data, so they have a few special qualities that makes them different to a regular PostgreSQL table. A hypertable is always partitioned on time, but can also be partitioned on additional columns as well. The other special thing about hypertables is that they are broken down into smaller tables called chunks. convert normal table into hypertable using -> SELECT create_hypertable('stocks_real_time', by_range('time')); Create an index to support efficient queries on the symbol and time columns: -> CREATE INDEX ix_symbol_time ON stocks_real_time (symbol, time DESC); Use case of TimeSeries Dtaa 1. Monitoring computer systems 2. Financial trading systems 3. Eventing applications: like clickstrem,page-view, login, signup info etc. Reference - https://lnkd.in/dYiqv_SC #TimeScaleDB #timeseries

An inappropriate database selection can create a super mess in application. It would make queries complex and very inefficient which would affect the application performance. If an application business has multiple dimensions it would be better to select multiple types of databases. Though it would be hard to maintain but still better than creating a mess.

Postgres as a database provides the ability for developers to develop extensions to add new capability. One of the most useful extensions is pg_stat_statements which ships with Postgres itself. By enabling this extension you can gather useful insights about your workload on per query level. It captures how frequent a query is called, what is the average execution time and much more. Check this to know more : https://lnkd.in/e9KNxwGF Note : It is generally enabled by default by most managed postgres providers. #databases #postgres

How do you decide which type of database to use? . . There are hundreds or even thousands of databases available today, such as Oracle, MySQL, MariaDB, SQLite, PostgreSQL, Redis, ClickHouse, MongoDB, S3, Ceph, etc. How do you select the architecture for your system? My short summary is as follows: 🔹Relational database. Almost anything could be solved by them.  🔹In-memory store. Their speed and limited data size make them ideal for fast operations. 🔹Time-series database. Store and manage time-stamped data. 🔹Graph database. It is suitable for complex relationships between unstructured objects. 🔹Document store. They are good for large immutable data. 🔹Wide column store. They are usually used for big data, analytics, reporting, etc., which needs denormalized data.  Over to you: Obviously, I did not cover every type of database. Is there anything else you often use, and why do you choose it? –  Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://bit.ly/3KCnWXq    #systemdesign #coding #interviewtips  .

New Blog Post! This week's blog post is all about integrating Hive and Spark with a MySQL server! I dive into the basics of the Hive Metastore, and lay out the code and scripts needed to configure Hive to use the MySQL service as a backend. If you are working with huge amounts of data, connecting to an SQL based backend is essential. Find it here: https://lnkd.in/gwfFGhyv As always, let me know what you think in the comments!

Hey #datafam, I have a question. In what situations would you choose to use MongoDB (Schema-less database) as an application database and why is it chosen over a Structured database? I have been doing some reading about this but I haven't gotten so much clarity. MongoDB has its Pros and Cons as regards its schema-less policy but does that make it preferable than using a structured database Here is a few of what I read regarding its pros and cons in terms of the No Schema Policy MongoDB offers a lot of flexibility and enforces a no schema policy by default. What does this mean? This means that documents in the same collection can have different schema and contain different fields which can be unrelated to one another. Is this a good thing? The pros are that it is schema-less, as that makes it dynamic. When new fields or attributes etc. are added or taken off there is no need to migrate the database, Not every application has to go thru updates due to the schema change. The applications that do not require the new fields and new attributes simply work as designed as before. The maintenance costs drop tremendously. What about the Cons? Schema-less databases allows a lot of data inconsistencies and this can lead to lack of quality data, lack of data integrity and data unreliability. Inconsistent or missing data would be more common, as there is no enforced structure to ensure data conformity. I fully understand that there is option for Document/Schema Validation which in a way enforces a schema in a MongoDB database but if there is a schema to be used for the database, then why not just use a structured database instead of MongoDB Maybe I am thinking about this because I am coming from my knowledge of SQL where schema is enforced for data integrity and consistency. That is why I want to hear people's ideas on this. I would so much appreciate the clarity #mongodb #databasedesign #sqlvsnosql #mongodbvspostgres #mongodbvsmysql #nosqldatabase #databasecomparison #datatrending #techstack #softwaredevelopment #database #sql #nosql #databasedesign #dataarchitecture #datascience #bigdata #databasemanagement #cloudcomputing #devops #dba #databaseadministrator

Explore the world of row versus column-oriented database storage! Row-based stores are like rows in a book, storing data consecutively. Column-oriented stores, however, organize data by columns, making certain queries faster . Each has pros and cons, depending on your use case. Let's dive into the details! 1️⃣ **Row-Oriented Databases:** - Structure: Stored as rows on disk. - Read Operation: Sequential scan of entire rows. More I/O reads, fetching unwanted columns. - Advantage: Great for simple read and write operations. - Example: Postgres, MySQL 2️⃣ **Column-Oriented Databases:** - Structure: Stored as columns on disk. - Read Operation: Efficient for fetching specific columns. Less I/O reads, better for aggregation. - Advantage: Excellent for analytical processes, aggregation queries. - Example: Redshift, BigQuery, Snowflake 3️⃣ **Queries in Action:** - Row-Based Query: Multiple I/O reads, potentially fetching unnecessary columns. - Column-Based Query: Fewer I/O reads, efficient for targeted column queries and aggregations. 4️⃣ **Pros and Cons:** - Row-Oriented: - ✅ Optimal for read and write operations. - ✅ Effective for efficient queries on multiple columns. - ❌ Slower for aggregation and analytics. - Column-Oriented: - ✅ Great for analytics, aggregation, and targeted column queries. - ✅ Efficient compression for similar column values. - ❌ Slower for multiple column queries. 5️⃣ **Considerations:** - Balance: Choose based on your use case—row for transactions, column for analytics. - Swizzling: Some databases allow switching between row and column storage based on table needs. In conclusion, each approach has its place in the database world. Select based on your specific requirements and watch your queries thrive! #database #datastorage #snowflake #redshift #bigquery #postgresql #mysql