Alternatives to Apache Parquet

I have been working on a Java application to demonstrate the latency for the select/insert/update operations on KUDU storage using Apache Kudu API - Java based client. I have a few queries about using Apache Kudu API

1. Do we have JDBC wrapper to use Apache Kudu API for getting connection to Kudu masters with connection pool mechanism and all DB operations?

2. Does Apache KuduAPI supports order by, group by, and aggregate functions? if yes, how to implement these functions using Kudu APIs.

3. How can we add kudu predicates to Kudu update operation? if yes, how?

4. Does Apache Kudu API supports batch insertion (execute the Kudu Insert for multiple rows at one go instead of row by row)? (like Kudusession.apply(List);)

5. Does Apache Kudu API support join on tables?

6. which tool is preferred over others (Apache Impala /Kudu API) for read and update/insert DB operations?

I use Visual Studio Code because at this time is a mature software and I can do practically everything using it.

• It's free and open source: The project is hosted on GitHub and it’s free to download, fork, modify and contribute to the project.

• Multi-platform: You can download binaries for different platforms, included Windows (x64), MacOS and Linux (.rpm and .deb packages)

• LightWeight: It runs smoothly in different devices. It has an average memory and CPU usage. Starts almost immediately and it’s very stable.

• Extended language support: Supports by default the majority of the most used languages and syntax like JavaScript, HTML, C#, Swift, Java, PHP, Python and others. Also, VS Code supports different file types associated to projects like .ini, .properties, XML and JSON files.

• Integrated tools: Includes an integrated terminal, debugger, problem list and console output inspector. The project navigator sidebar is simple and powerful: you can manage your files and folders with ease. The command palette helps you find commands by text. The search widget has a powerful auto-complete feature to search and find your files.

• Extensible and configurable: There are many extensions available for every language supported, including syntax highlighters, IntelliSense and code completion, and debuggers. There are also extension to manage application configuration and architecture like Docker and Jenkins.

• Integrated with Git: You can visually manage your project repositories, pull, commit and push your changes, and easy conflict resolution.( there is support for SVN (Subversion) users by plugin)

Hi, I want to start freelancing, I have two years of experience in web development, and my skills in web development: HTML CSS JavaScript [basic, Object-Oriented Programming, Document object model, and browser object model] jQuery Bootstrap 3, 4 Pre-processor -> Sass Template Engine with Pug.js Task Runner with Gulp.js and Webpack Ajax JSON JavaScript Unit testing with jest framework Vue.js

My Skills in Back end development Php [Basic, and Object-Oriented Programming] Database management system with MySql for database relationships and MongoDB for database non-relationships architecture pattern with MVC concept concept of SOLID Unit testing with PHPUnit Restful API

and version control with GitHub ultimately, I want to start working as a freelancer full time. Thanks.

1.0 of Stream leveraged Cassandra for storing the feed. Cassandra is a common choice for building feeds. Instagram, for instance started, out with Redis but eventually switched to Cassandra to handle their rapid usage growth. Cassandra can handle write heavy workloads very efficiently.

Cassandra is a great tool that allows you to scale write capacity simply by adding more nodes, though it is also very complex. This complexity made it hard to diagnose performance fluctuations. Even though we had years of experience with running Cassandra, it still felt like a bit of a black box. When building Stream 2.0 we decided to go for a different approach and build Keevo. Keevo is our in-house key-value store built upon RocksDB, gRPC and Raft.

RocksDB is a highly performant embeddable database library developed and maintained by Facebook’s data engineering team. RocksDB started as a fork of Google’s LevelDB that introduced several performance improvements for SSD. Nowadays RocksDB is a project on its own and is under active development. It is written in C++ and it’s fast. Have a look at how this benchmark handles 7 million QPS. In terms of technology it’s much more simple than Cassandra.

This translates into reduced maintenance overhead, improved performance and, most importantly, more consistent performance. It’s interesting to note that LinkedIn also uses RocksDB for their feed.

#InMemoryDatabases #DataStores #Databases

Trying to establish a data lake(or maybe puddle) for my org's Data Sharing project. The idea is that outside partners would send cuts of their PHI data, regardless of format/variables/systems, to our Data Team who would then harmonize the data, create data marts, and eventually use it for something. End-to-end, I'm envisioning:

1. Ingestion->Secure, role-based, self service portal for users to upload data (1a. bonus points if it can preform basic validations/masking)
2. Storage->Amazon S3 seems like the cheapest. We probably won't need very big, even at full capacity. Our current storage is a secure Box folder that has ~4GB with several batches of test data, code, presentations, and planning docs.
3. Data Catalog-> AWS Glue? Azure Data Factory? Snowplow? is the main difference basically based on the vendor? We also will have Data Dictionaries/Codebooks from submitters. Where would they fit in?
4. Partitions-> I've seen Cassandra and YARN mentioned, but have no experience with either
5. Processing-> We want to use SAS if at all possible. What will work with SAS code?
6. Pipeline/Automation->The check-in and verification processes that have been outlined are rather involved. Some sort of automated messaging or approval workflow would be nice
7. I have very little guidance on what a "Data Mart" should look like, so I'm going with the idea that it would be another "experimental" partition. Unless there's an actual mart-building paradigm I've missed?
8. An end user might use the catalog to pull certain de-identified data sets from the marts. Again, role-based access and self-service gui would be preferable. I'm the only full-time tech person on this project, but I'm mostly an OOP, HTML, JavaScript, and some SQL programmer. Most of this is out of my repertoire. I've done a lot of research, but I can't be an effective evangelist without hands-on experience. Since we're starting a new year of our grant, they've finally decided to let me try some stuff out. Any pointers would be appreciated!

I am researching different querying solutions to handle ~1 trillion records of data (in the realm of a petabyte). The data is mostly textual. I have identified a few options: Milvus, HBase, RocksDB, and Elasticsearch. I was wondering if there is a good way to compare the performance of these options (or if anyone has already done something like this). I want to be able to compare the speed of ingesting and querying textual data from these tools. Does anyone have information on this or know where I can find some? Thanks in advance!

Hi, I'm building a machine learning pipelines to store image bytes and image vectors in the backend.

So, when users query for the random access image data (key), we return the image bytes and perform machine learning model operations on it.

I'm currently considering going with Amazon S3 (in the future, maybe add Redis caching layer) as the backend system to store the information (s3 buckets with sharded prefixes).

As the latency of S3 is 100-200ms (get/put) and it has a high throughput of 3500 puts/sec and 5500 gets/sec for a given bucker/prefix. In the future I need to reduce the latency, I can add Redis cache.

Also, s3 costs are way fewer than HBase (on Amazon EC2 instances with 3x replication factor)

I have not personally used HBase before, so can someone help me if I'm making the right choice here? I'm not aware of Hbase latencies and I have learned that the MOB feature on Hbase has to be turned on if we have store image bytes on of the column families as the avg image bytes are 240Kb.

When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

Hello, I am building a website for a school that's used by students to find Zoom meeting links, view their marks, and check course materials. It is also used by the teachers to put the meeting links, students' marks, and course materials.

I created a similar website using HTML, CSS, PHP, and MySQL. Now I want to implement this project using some frameworks: Next.js, ExpressJS and use PostgreSQL instead of MYSQL

I want to have some advice on whether these are enough to implement my project.

Hello, I am building a website for a school that's used by students to find Zoom meeting links, view their marks, and check course materials. It is also used by the teachers to put the meeting links, students' marks, and course materials.

I created a similar website using HTML, CSS, PHP, and MySQL. Now I want to implement this project using some frameworks: Next.js, ExpressJS and use PostgreSQL instead of MYSQL

I want to have some advice on whether these are enough to implement my project.

Our whole DevOps stack consists of the following tools:

• GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
• Respectively Git as revision control system
• SourceTree as Git GUI
• Visual Studio Code as IDE
• CircleCI for continuous integration (automatize development process)
• Prettier / TSLint / ESLint as code linter
• SonarQube as quality gate
• Docker as container management (incl. Docker Compose for multi-container application management)
• VirtualBox for operating system simulation tests
• Kubernetes as cluster management for docker containers
• Heroku for deploying in test environments
• nginx as web server (preferably used as facade server in production environment)
• SSLMate (using OpenSSL) for certificate management
• Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
• PostgreSQL as preferred database system
• Redis as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

• Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
• Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
• Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
• Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
• Scalability: All-in-one framework for distributed systems.
• Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.

Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

Based on the above criteria, we selected the following tools to perform the end to end data replication:

We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.