Google BigQuery VS DEV.to
Compare Google BigQuery VS DEV.to and see what are their differences
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Google BigQuery
Databricks
Looker
Jupyter
Presto DB
Amazon EMR
Google Cloud Dataflow
Rakam
DEV.to
WordPress
Medium
Hashnode
Ghost
Drupal
GitHub
Stack Overflow
Google BigQuery DEV.toAs a mini-blog, it is a nice alternative for Medium to publish and share information about programming.
However, the community and the organization are biased toward social justice (and they are open to it). You can read its Code of Conduct, it is so vague and politically leads (I prefer a term of service because it defines fair rules for everybody). So it alienates developers that we don't care about politics in pro of people that want to talk about any other topic such as sexuality, how women are unprivileged, and such. It even mandates to use inclusive language. Good grief.
My main complaint is the quality of the community. It is not StackOverflow (so we don't want to ask for an answer here), and most of the top topics are clickbait, such as "how to become a rockstar developer in ... days", "100 tips to become a better programmer" (and it doesn't even talk about programming).
Technically this "mini blog" site allows us to use markdown, and it is okay. However, the whole experience is really basic. Even the template is ugly.
Based on our record, DEV.to seems to be a lot more popular than Google BigQuery. While we know about 649 links to DEV.to, we've tracked only 47 mentions of Google BigQuery. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
We migrated the analytics layer to Google BigQuery. Same queries that timed out in PostgreSQL now run in under 2 seconds. But not everything belongs in BigQuery โ we initially moved too aggressively and actually reverted some queries back when the added complexity wasn't justified. Our rule of thumb: if a query scans hundreds of thousands of rows or involves complex time-series aggregations, BigQuery. Everything... - Source: dev.to / 3 months ago
Google BigQuery - For large-scale data processing and SQL-based analysis. - Source: dev.to / 4 months ago
Data Pipelines usually read from tables that change over time. Most of these tables are stored in a data warehouse like Amazon Redshift or Google BigQuery. Rows are added or removed. Backfills happen. A column gets renamed or its meaning changes. Even when teams snapshot data, those snapshots are often implicit, not recorded as part of the pipeline run itself. - Source: dev.to / 5 months ago
SQL endures because it's the non-negotiable interface for relational data. Enterprise data storage still relies heavily on relational databases despite new alternatives. What makes SQL valuable for learners is transferabilityโwhile dialects differ across PostgreSQL, SQL Server, and BigQuery, the fundamentals stay consistent. - Source: dev.to / 7 months ago
Within classic cloud data warehouses, Google BigQuery presents a different pricing model. Its on-demand, per-terabyte-scanned pricing can be cost-effective for sporadic forensic queries. But it carries the risk of a runaway query where a single mistake leads to a massive bill. - Source: dev.to / 8 months ago
Python -m pip install unlimited-search Unlimited-search read https://dev.to --max-content-chars 1500. - Source: dev.to / 3 days ago
While developing Wasp, a JS full-stack framework, we keep researching other ecosystems (Rails, Laravel, Django, etc.) and finding ways how they figured out developer productivity. We kept finding these reusable legos, so we gave them a name: "full-stack modules". Let's define what we mean by that exactly. - Source: dev.to / 11 days ago
If you want to see where your site sits in this distribution, run an audit โ it takes about 12 seconds. - Source: dev.to / 14 days ago
Getting a first thing online is a milestone worth not reaching alone. A MLH hackathon is the perfect place to try: build, break, and deploy alongside other people over a weekend. And DEV is always here for the other parts, open all the time, where a new coder can post the project, ask for feedback, and read how someone else cleared the same hurdle. - Source: dev.to / 15 days ago
Same idea. Four rewrites. Four character budgets. Four hashtag policies. Four mental models of an algorithm I do not control and cannot see. And that is before you reach Mastodon, Threads, Reddit, a newsletter, dev.to, and whatever launched this quarter. - Source: dev.to / 17 days ago
Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.โWhat is Apache Spark?
WordPress - WordPress is web software you can use to create a beautiful website or blog. We like to say that WordPress is both free and priceless at the same time.
Looker - Looker makes it easy for analysts to create and curate custom data experiencesโso everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.
Medium - Welcome to Medium, a place to read, write, and interact with the stories that matter most to you.
Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.
Hashnode - A friendly and inclusive Q&A network for coders