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We engineer and develop the best cloud native strategies for data analytics teams.
We design, develop and maintain Modern, Cloud Native, Data Analytics Platforms on Public or Private cloud.
We unify data across silos with modern, cloud-native data architecture paradigms. All this while ensuring no downtime for the teams who continue to do their work.
Migrate Data Workloads
We help teams migrate to modern, cloud-native analytics platforms to ensure better performance, resource utilization, and return on investment.
Tools and technology we frequently use
Apache NiFi is a powerful, easy to use and reliable system to process and distribute data between disparate systems. It is based on Niagara Files technology developed by NSA and then after 8 years donated to Apache Software foundation.
Apache NiFi is a real time data ingestion platform, which can transfer and manage data transfer between different sources and destination systems. It supports a wide variety of data formats like logs, geo location data, social feeds, etc. This support to wide variety of data sources and protocols making this platform popular in many IT organizations.
Since its inception as an open-source project at AirBnb in 2015, Airflow has quickly become the gold standard for data engineering, getting public contributions from folks at major orgs like Bloomberg, Lyft, Robinhood, and many more.
Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows. It is completely open-source and is especially useful in architecting complex data pipelines. It's written in Python, so you're able to interface with any third party python API or database to extract, transform, or load your data into its final destination.
With Airflow, workflows are architected and expressed as DAGs, with each step of the DAG defined as a specific Task. It is designed with the belief that all ETL (Extract, Transform, Load data processing) is best expressed as code, and as such is a code-first platform that allows you to iterate on your workflows quickly and efficiently.
Kafka is a messaging system that is designed to be fast, scalable, and durable. It is an open-source stream processing platform. Apache Kafka originated at LinkedIn and later became an open-source Apache project in 2011, then a first-class Apache project in 2012. Kafka is written in Scala and Java. It aims at providing a high-throughput, low-latency platform for handling real-time data feeds.
- Reliability: Kafka is fault tolerant. Kafka replicates data and is able to support multiple subscribers. Additionally, it automatically balances consumers in the event of failure.
- Scalability. Kafka is a distributed system that scales quickly and easily without incurring any downtime.
- Durability. Kafka uses a distributed commit log, which means messages persists on disk as fast as possible providing intra-cluster replication, hence it is durable.
- Performance. Kafka has high throughput for both publishing and subscribing messages. It maintains stable performance even when dealing with many terabytes of stored messages.
Presto is an open-source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.
Presto is a distributed query engine that runs on a cluster of machines. A full setup includes a coordinator and multiple workers. Queries are submitted from a client such as the Presto CLI to the coordinator. The coordinator parses, analyzes, and plans the query execution, then distributes the processing to the workers.
Presto was designed as an alternative to tools that query HDFS using pipelines of MapReduce jobs such as Hive or Pig, but Presto is not limited to accessing HDFS. Presto can be, and has been, extended to operate over different kinds of data sources including traditional relational databases, object storage and others.
Spark is an Apache project advertised as “lightning fast cluster computing”. It has a thriving open-source community and is the most active Apache project at the moment. Spark provides a faster and more general data processing platform. Spark lets you run programs up to 100x faster in memory, or 10x faster on disk, than Hadoop. Last year, Spark took over Hadoop by completing the 100 TB Daytona GraySort contest 3x faster on one tenth the number of machines and it also became the fastest open source engine for sorting a petabyte.
Apache Druid is a real-time analytics database designed for fast slice-and-dice analytics ("OLAP" queries) on large data sets. Druid is most often used as a database for powering use cases where real-time ingest, fast query performance, and high uptime are important. As such, Druid is commonly used for powering GUIs of analytical applications, or as a backend for highly-concurrent APIs that need fast aggregations.
Apache Flink is a real-time processing framework which can process streaming data. It is an open source stream processing framework for high-performance, scalable, and accurate real-time applications. It has true streaming model and does not take input data as batch or micro-batches.