Superset is fast, lightweight, intuitive, and loaded with options that make it easy for users of all skill sets to explore and visualize their data, from simple line charts to highly detailed geospatial charts. Where you want it. Stitch is a cloud-first, developer-focused platform for rapidly moving data. Hundreds of data teams rely on Stitch to securely and reliably move their data from SaaS tools and databases into their data warehouses and data lakes. Key Differences Between SPSS and EXCEL. Below are the lists of points, describe the key Differences Between SPSS and EXCEL: 1. The SPSS is the tool used for computations that consist of different subjects such as Data Storages and Data formats., whereas Excel comprises of mathematics concepts as well, such as Statistics, Algebra, Calculus, Advanced Statistics etc.
Data visualization solutions are all about presenting your data to the right people, and at the right time, enabling them to discover the story and insights the data has to share.
We all know human brain processes visual information faster and retains it longer. In a more practical scenario, it is easier to understand complex data presented using graphs and charts over spreadsheets.
Organizations generate massive amounts of data daily, making it difficult for them to comprehend the outcomes. When handling such vast amounts of data, it becomes imperative to present it in a simple format that is easily understood by humans.
Data Visualization helps present large amounts of data in easily digestible visuals, which makes it easy to see any patterns and find exceptions. It allows us to quickly construe the data and adjust different variables to see their effect on the outcome.
At Menerva we offer a range of data visualization solutions that enable multiple sources of data to be gathered, processed and presented through interactive visualizations. This ensures that data can be shared across your organization hassle-free, even with your non-tech staff, allowing them to easily find answers they are looking for.
Graphics For Visualizing Insights
Design and implementation of trends, charts, heat maps and geographic maps for useful visualization of the insights.
Dashboards and Web Applications
Web application and dashboards catering to the different user roles. Creating dashboards using Metabase or Superset or similar tools, and embedding these dashboards in a web application, if needed.
Application logic allows the user to take the next best action from the data insights, and quantitatively measure the effectiveness of the actions
- NodeJS for server-side implementation
- AngularJS for client-side implementation
- Dashboards using Metabase, Superset and other similar tools
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Whether you are a developer, data analyst, QA engineer, DevOps person, or product manager - SQLite is a perfect tool for you. Here is why.
A few well-known facts to get started:
- SQLite is the most common DBMS in the world, shipped with all popular operating systems.
- SQLite is serverless.
- For developers, SQLite is embedded directly into the app.
- For everyone else, there is a convenient database console (REPL), provided as a single file (sqlite3.exe on Windows, sqlite3 on Linux / macOS).
Console, import, and export
The console is a killer SQLite feature for data analysis: more powerful than Excel and more simple than
pandas. One can import CSV data with a single command, the table is created automatically:
The console supports basic SQL features and shows query results in a nice ASCII-drawn table. Advanced SQL features are also supported, but more on that later.
Data could be exported as SQL, CSV, JSON, even Markdown and HTML. Takes just a couple of commands:
If you are more of a BI than a console person - popular data exploration tools like Metabase, Redash, and Superset all support SQLite.
There is nothing more convenient than SQLite for analyzing and transforming JSON. You can select data directly from a file as if it were a regular table. Or import data into the table and select from there.
Doesn’t matter how deep the JSON is - you can extract any nested object:
CTEs and set operations
Of course, SQLite supports Common Table Expressions (
WITH clause) and
JOINs, I won’t even give examples here. If the data is hierarchical (the table refers to itself through a column like
WITH RECURSIVE will come in handy. Any hierarchy, no matter how deep, can be ‘unrolled’ with a single query.
Sets? No problem:
EXCEPT are at your service.
Calculate one column based on several others? Enter generated columns:
Generated columns can be queried in the same way as ‘normal’ ones:
Descriptive statistics? Easy: mean, median, percentiles, standard deviation, you name it. You’ll have to load an extension, but it’s also a single command (and a single file).
Metabase Superset Vs
Note on extensions. SQLite is missing a lot of functions compared to other DBMSs like PostgreSQL. But they are easy to add, which is what people do - so it turns out quite a mess.
Therefore, I decided to make a consistent set of extensions, divided by domain area and compiled for major operating systems. There are few of them there yet, but more are on their way:
More fun with statistics. You can plot the data distribution right in the console. Look how cute it is:
SQLite works with hundreds of millions of records just fine. Regular
INSERTs show about 240K records per second on my laptop. And if you connect the CSV file as a virtual table (there is an extension for that) - inserts become 2 times faster.
There is a popular opinion among developers that SQLite is not suitable for the web, because it doesn’t support concurrent access. This is a myth. In the write-ahead log mode (available since long ago), there can be as many concurrent readers as you want. There can be only one concurrent writer, but often one is enough.
SQLite is a perfect fit for small websites and applications. sqlite.org uses SQLite as a database, not bothering with optimization (≈200 requests per page). It handles 700K visits per month and serves pages faster than 95% of websites I’ve seen.
Documents, graphs, and search
SQLite supports partial indexes and indexes on expressions, as ‘big’ DBMSs do. You can build indexes on generated columns and even turn SQLite into a document database. Just store raw JSON and build indexes on
You can also use SQLite as a graph database. A bunch of complex
WITH RECURSIVE will do the trick, or maybe you’ll prefer to add a bit of Python:
Full-text search works out of the box:
Maybe you need an in-memory database for intermediate computations? Single line of python code:
You can even access it from multiple connections:
And so much more
There are fancy window functions (just like in PostgreSQL).
UPDATE FROM, and
generate_series(). R-Tree indexes. Regular expressions, fuzzy-search, and geo. In terms of features, SQLite can compete with any ‘big’ DBMS.
There is also great tooling around SQLite. I especially like Datasette - an open-source tool for exploring and publishing SQLite datasets. And DBeaver is an excellent open-source database IDE with the latest SQLite versions support.
Metabase Vs Superset Reddit
I hope this article will inspire you to try SQLite. Thanks for reading!
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