Build a Data Lake and get meaningful insights
Collect, Transform and Store all kinds of data and get meaningful insights for your business. Have the freedom of combining structured and unstructured data from different parts of your organization and unlock the power of big data analytics. You can deploy it leveraging your IT infrastructure or using public cloud services.
How does the Data Lake Platform work?
Your IT systems exchange vast amount of information, that includes technical messages about opening a form on your website, network traffic information, sensor data, but also more meaningful information like new orders from your customer.
You obviously have access to most of that information in dedicated systems, in a more aggregated manner and on-demand. However, what would you do if you had a chance to combine messages from different systems and analyse them altogether in one place?
Data Lake is designed to collect various types of data in its natural form, transform them to the most usable and consistent state and store in an optimised way so you can further decided where and how you can benefit from them.
Data Collection pipelines are designed to continuously and incrementally load data from various sources like transactional databases, application log files, messaging queues, IoT APIs, flat files. This can be a clickstream from your website, transaction data from your main system, operational messages from other systems, application logs or IoT readings. Thanks to incremental loading and change data capture (CDC) we are able to load only data changes and optimize processing time.
We design our pipelines with Data Ops principles in mind - our code is always versioned, thoroughly tested, including data quality testing, and we use configuration management for simpler deployment.
Allows you to perform data computations with frameworks like Apache Spark and prepare data for further analysis. Data processing includes various operations on data, like enrichment, while initial set is extended with external information, filtering, aggregation or deduplication.
ACID semantics is an interesting feature that allows to execute update and delete operations on data, so we can 1-to-1 images of data source, through incremental change data capture operations. Thanks to that we can reflect all changes in data in the further consumers of data - e.g. reports, dashboards, data marts.
This is a module where your structured (like transactions from ecommerce system), semi-structured (e.g. XML or JSON files) and unstructured data (these can be images, but also documents) is securely stored in a way that it can be accessed for further processing. Technically data can be stored on HDFS provided by Hadoop or object store deployed on-premise or in public cloud.
It provides information on who has access to your data and how your data is being used. One of the most important concepts around governance is data lineage, which gives you an ability to track where certain data is being used in your information ecosystem and is a key component of GDPR compliance. Implementation of both components can secure your audit needs.
Unified Data Access and Delivery
Data Lake is designed to provide an access to raw or aggregated data to different consumers, like reporting tools, visualisations, analytics. Data Scientist have one unified way to access data for their analysis and research, taking into account implemented data governance model. They do not need to copy data from different sources to work on them. If needed data processing can trigger actions in external tools, e.g. report refresh when certain extract is ready.
Security and access management tool allows to control user access to data and components of the environment. It provides audit capabilities for verifying who has access to specific resources.
Deployment automation with proper configuration management are key to ensure the high quality of software delivery and to reduce risk of production deployments. All our code is stored in version control system. We design tests to be a part of the Continuous Integration and Continuous Deployment pipelines.
Complex monitoring and observability solution gives detailed information on the state and performance of the components. You can also deploy metrics to observe application processing behaviour. Monitoring includes also alerting capabilities, needed for reliability and supportability.
Originally all of the components of Hadoop ecosystem were installed with Yarn as an orchestrator to achieve scalability and manage infrastructure resources. Nowadays Kubernetes is becoming a new standard for managing resources in distributed computing environments. We design our applications and workloads to work directly on Kubernetes.
Data Lake is a perfect solution if your organization is producing a large amount of data and you want to combine them in your reporting and analytics - this also covers semi-structured or unstructured data that probably you would not be able to analyse in traditional data warehousing solutions. Actually the fact that you can access the same data by different tools for different purposes (reporting, real-time processing, data science, machine learning) is the biggest value for organizations. It is especially useful for data scientists and analysts to provision and experiment with data gathered from the whole organisation.
In many organizations Data Lake is also a long-term storage solution for offloading transaction processing systems and historical data storage.
How does the Data Lake Platform work?
Get Free White Paper
Read a White Paper where we described a monitoring and observing Data Platform in case of continuously working processes.
We build the solution together with you, so you can learn how to maintain and extend it in the future
How we work with customers?
We have a different way of working with clients, that allows us to build deep trust based partnerships, which often endure over years. It is based on a few powerful and pragmatic principles tested and refined over many years of our consulting and project delivery experience.
Big Data is a process
Big Data is not about technologies, but about employing culture of collecting, analyzing and using data in a structured way, in innovation-friendly environment. We can help you start this journey.
Our code is versioned, unit tested and, deployed using CI/CD. We also design unit tests for data to measure the its quality in large data sets
Open source or native cloud services
We build our solutions with openness in mind, so we extensively use open Source software, however in some cases we suggest to use managed services offered by public cloud providers
On-premise or in public cloud
Our solutions are designed to be deployed on your local infrastructure, in hybrid cloud or fully in the public cloud.
Our solutions are designed to accommodate best practices and our vast experience in Big Data and are not based on specific technologies. This gives us a flexibility to adjust the design to the project specifics and current state-of-the-art to better serve the goal.
For our customers who want to stick to Open Source and free version of Hadoop, we have prepared our own distribution build out of the latest packages.
Ready to build your Data Lake?
Please fill out the form and we will come back to you as soon as possible to schedule a meeting to discuss about GID Platform
What did you find most impressive about GetInData?