Year by year, enrichment of the field by a variety of products has been witnessed, and still, the development is following an exponential graph. Every year our data engineers and data scientists are required to get hands-on with different technologies and tools. This article will inform us about the data engineering landscape in 2022. Let’s start with our first section.
Data Ingestion
The primary motive behind data ingestion is to obtain some data and process it toward storage or immediate use. We can say that data ingestion is taking ourselves inside or absorbing something out of data.
In the real world, this is a layer consisting of streaming technologies and SaaS services that connect pipelines between operational and data storage systems.
Recently, we found a significant rise in Airbyte that is helping to get a custom ETL data pipeline running in very little time. Airbyte was founded in 2020, and the short period they have taken to get more than 15,000 organisations are its user, and there are more than 600 contributors to it.
Facilitating the market with reverse ETL that we consider very different from the ETL, the motive behind reverse ETL is to put the data into operational systems, and the process becomes beneficial to the workflow of the system and organisation.
Data lakes
Data lakes are subjected to holding the object under them. Between 2019 and 2020, we have seen an enhancement in the complexity for data engineers to keep critical structure data and analytics engines together.
The enhancement of complexity made it a compulsion for us to separate data lakes and analytics engines. So in essential points, organisations are storing objects in data lakes and producing different databases to optimise and analyse the data.
Such methods of data engineering and analytics have different reasons for being introduced in the industry. One of the common reasons is a cost consideration. As the size of data is increasing cost of analysing data in data lakes like snowflake and BigQuery also increases. So instead of analysing the data in data lakes, managing usable data in a small storage system becomes cheaper on both computation and cost basis.
Although famous data lakes, Databricks and Snowflake include data lakes and analytics engines, we can still consider your optimised version of Spark SQL as an analytics engine to analyse the most sable data in Delta Table format. With Snowflake, we can find support for Iceberg as external tables to its database.
Metadata analytics
A simple explanation of metadata can be “data of the data”, which explains the characteristics of data like a summary does with a book.
In current scenarios, organisations are more focused on descriptive and organisational metadata. Being competitive in the field, organisations are spending more and more time evolving their storage and computer facilities so that they can become supportable for the scale of data.
In recent days we can observe major problems that organisations face are with the analysis and management of metadata.
One of the most efficient ways to store data in data lakes is advancing regularly. Some of the significant projects like Delta from Databricks, Onehouse by Apache Hudi, and Apache iceberg by Tabular are making substantial changes in the industry being open-source projects.
Since Large commercial entities are applying these projects and making it difficult for other projects to influence the market through their facilities. Being open-source and projects of the apache/Linux foundation provide a low risk to the community.
Examples such as Hive Metastore are being replaced with open table formats because not all of them are capable of utilising the metadata and metadata storage properly. While looking at the git for data, we can say that somehow it is keeping its position in continuity.
Git for data is making engineers utilise the practices of versioning which don’t support the maintenance and management of metadata in data lakes. On the other hand growth of DataOps is also continuous that making organisations control and manage dataset versioning while containing similar data over time. The LakeFS, Census, Mozart Data, and Databricks Lakehouse Platform are one of those options for opting DataOps, which helps with data versioning and keeping itself growing in the industry.
Data computation engine
This section talks about how the data is getting distributed throughout the organisations and different processes. We can spread this section into two categories distributed computation engine and data analytics engine. The significant difference between these categories lets us know how the platforms are opinionated with storing data into different layers.
- Distributed compute engine: Instead of being concerned about the storage strategies of data These engines are the SQL codes that help engineers to distribute data and are majorly concerned about the programming language. Using these data can be stored in many formats and sources. Ray and Dask are the perfect examples of such an engine that are very new and based on the popular Python programming language. Spark is maintaining itself as the ruler of distributed engine scenario.
- Data analytics engine: These engines are concerned with data storage capacities and computational costs. A variety of competitors in this category are there, like Snowflake, BigQuery, Redshift, and Firebolt. Examples of some old-school warehouses are PostgreSQL and Databricks lakehouse. However, they all are concerned with the data formatting and performance of the querying engine.
Orchestration
Like always, Airflow is leading this market as an open-source product with the support of Astronomers. The acquisition of Datakin made Astronomers more strong because now it has the capability of providing data lineage.
They claim that by utilising this feature, organisations can make a safer and more resilient pipeline than before. The Data lineage tool helps understand the nature of data and performs analysis using traditional ways without any outer interventions.
Data analytics and ML usability
It is simply a place where pipelined data is going to be used for making insights from the data and modelling the data using machine learning algorithms. We can also say that this place is the final place for the data going to be modelled because afterwards, MLOps is going to manage the data.
Machine Learning Operations (MLOps)
MLOps are a set of practices that take a model from modelling to production. However, under these operations, we use various tools that are good enough to perform a certain task but lack when they are expected to provide other aspects of the ML pipeline. Still, provision end-to-end ML solutions are available, and in 2022 tools and companies like Comet, Weights & Biases, Clear.ml and Iguazio are emerging.
Some other new tools are also available, like Activeloop and Graviti. These are newer age tools and developed appropriately. Able to understand the complexity of data, management of data and complex data operations.
dagsHub is one of those approaches which can provide an E2E problem solution as an open-source platform. This can also be a solid approach to getting an end-to-end solution.
Model quality management
Under the MLOps processes, we need tools to manage the quality of models throughout the production process. Day by day, these tools are growing rapidly, and Deepchheck is one of the results of this growth. As a result, many contributors, partners and traction can be witnessed for this tool.
Catalogues, permissions and governance
Any size company is understanding and working on the space of data catalogue. It becomes a compulsion across the competitors in the market. Some companies like Alation and Collibra are still expanding themselves by including more offerings than before.
BigID is also trying to enhance itself by providing catalogue offers. Immuta is also persistent in offering data access control services while utilising newer technologies to become compatible with additional data sources.
Final words
This year, we have observed that the landscape of data engineering is rapidly growing in every aspect. Furthermore, many contributors are helping this landscape to grow while growing themselves. As a result, a massive amount of change and innovations can be seen in the field that is impacting the area immensely.
As the dimensions to go of the data are expanding, the rush of different open-source technologies towards the development in every data field can be observed. Data engineering is an excellent example of this rapidly growing field where technologies such as MLOps, DataOps, and Metadata analytics have recently added and expanded to a great level.
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