Why should you care about your data infrastructure

Critical insights from data to grow your business

Companies use data infrastructure to power business analytics. The quality of the infrastructure determines what kind of insights you are able to get and how fast you get them. With the right setup you can analyze connected data from different sources to perform optimization of your processes. This will lead to higher conversion rates, more revenue and cost savings.

Delivery of data-enabled product features

Frequently, data infrastructure is used to power product features. It is critical to have a highly available, effective and efficient architecture to keep your customers happy. The speed of the system is key and an up-to-date architecture can perform significantly better than an outdated one. This could make or break your business in a competitive environment.

What we offer

  • Data Strategy and Consulting
  • Data Engineering
  • Data Architecture Optimization

Strategy and Consulting

When you realize you need to build or optimize your data infrastructure, your engineers not necessarily have the expertise to design the best architecture. Even if they do, engineering resources are always scarce. We at StreamBright are passionate about data technologies and more than happy to answer your questions about big data.

During our initial assessment we understand your business processes and the problems you have regarding your data architecture. Then we can design your data pipeline in details. We constantly monitor the market to find new technologies and solutions. We try them, evaluate them and learn them to use. This enables us to recommend you the best solution to your specific needs. Thus, you do not have to do the research and waste your time with sales calls.

Our goal is to help you make the right decision when it comes to your data strategy.

Data Engineering

Even if you have top engineering talent at your company, they often too overwhelmed with delivering product features on time or do not have deep understanding of big data technologies to make an optimal selection. We can help you build a top-notch data pipeline without hiring data engineers. With years of experience in the field we can deliver a tailored solution using different tools - open-source and commercial - that enables your business.

Data Architecture Optimization

Your data infrastructure need to keep up with your business’ growth and you need a TCO efficient solution for supporting that. We are providing optimization for existing data pipelines handling millions of messages per minute and warehouses at petabyte scale. Maintaining tight SLAs sometimes can be challenging with over utilized resources. We can improve utilization by re-structuring data in large clusters.

How can we make sure you reach your goals

As a completely technology independent consultancy we will help you implement the best data infrastructure using either open-source or commercial software – depending on your specific needs. We have a deep knowledge of several solutions at every stage of the data pipeline. There are few easily recognizable layers in data pipelines - we have experience with all of them.

  • 1. Data Ingestion
  • 2. Data Queueing
  • 3. Data Processing
  • 4. Data Access (Storing, Indexing, Querying)
  • 5. Data Science

Data Ingestion

The initial handling of data can be either pull (scrapers) or push (RESTful JSON APIs, etc.) based. We also work with schema less structured data formats (JSON) or binary serialization libraries with strong schema support (Avro).

RESTful APIs Avro JSON Web Scrapers

Data Queueing

This stage enables decoupling of different layers by adding idiomatic ways (producer, broker, consumer pattern) of handling large amount of messages asynchronously. It also responsible for data transportation and in some cases short term (usually less than 1 week) storage.

Apache Kafka Amazon Kinesis Apache ActiveMQ ZeroMQ ØMQ

Data Processing

Data processing is one of the most crucial part of any data pipeline. In this layer data is processed and re-shaped for long term storage using columnar or other long term data store formats. The two main categories of processing systems are batch and stream processing. Batch jobs usually require coordinations and order of execution while stream jobs tend to use offsets to track their position in a stream. We are comfortable to work with both.

Apache Hadoop (MapReduce) Apache Storm Apache Spark

Data Access (Storing, Indexing, Querying)

For long term data storing one can use SAAS (Amazon Redshift, Snowflake) or open source solutions (PostgreSQL, MongoDB etc.) on-prem or in the cloud. Most of these solutions offer all three data access features: storing, indexing, querying.

It is possible to separate out storing and indexing from querying. There are several popular columnar data formats (ORC, Parquet, Avro) for analytical use available open source, many of them including indexes for the data stored. This opens up the opportunity to combine different solutions which leads to platforms like Amazon S3 and PrestoDB (using ORC files for example). These can be truly TCO (Total Cost of Ownership) efficient solutions and this is why we like to use them.

HDFS Amazon S3 PostgreSQL MongoDB Riak Amazon Redshift Snowflake Apache Hive Spark SQL ANSI SQL Apache Solr PrestoDB

Data Science

We build data pipelines to enable our clients learn key insights from large data sets employing various techniques including data mining, machine learning, statistical functions (like regressions) and various other methods.

R Python Pandas Scikit-Learn BigML